Guided self-organisation in open distributed systems

[no abstract

Channel acquisition and routing system for real-time cognitive radio sensor networks

The need for efficient spectrum utilization and routing has ignited interest in the Cognitive Radio Sensor Network (CRSN) paradigm among researchers. CRSN ensures efficient spectrum utilization for wireless sensor network. However, the main challenge faced by CRSN users have to deal with is the issue of service quality in terms of interference when using channels and degradation in multi-hop communication. This thesis proposes to overcome the interference due to contention and routing issues through the design of an efficient Channel Acquisition and Reliable routing System (CARS). CARS is designed to reduce carrier sense multiple access contention and enhance routing in CRSNs. CARS comprises of Lightweight Distributed Geographical (LDG), and Reliable Opportunists Routing (ROR) modules. LDG is a medium access control centric; cross-layer designed protocol to acquire a common control channel for signalling to determine the data channel. ROR is a network-centric cross-layer designed protocol to decide on a path for routing data packets. The result shows that LDG significantly reduces the overhead of media access contention and energy cost by at an average of 70% and 80% respectively compared to other approaches that use common control channel acquisition like Efficient Recovery Control Channel (ERCC) protocol. In addition, LDG achieves a 16.3% boost in the time to rendezvous on the control channel above ERCC and a 36.9% boost above Coordinated Channel Hopping (CCH) protocol. On the other hand, the virtual clustering framework inspired by ROR has further improved network performance. The proposed ROR significantly increases packet received at the sink node by an average of over 20%, reduces end-to-end latency by an average of 37% and minimizes energy consumption by an average of 22% as compared to Spectrum-aware Clustering for Efficient Multimedia routing (SCEEM) protocol. In brief, the design of CARS which takes the intrinsic characteristics of CRSNs into consideration helps to significantly reduce the energy needed for securing a control channel and to guarantee that end-to-end, real-time conditions are preserved in terms of latency and media content. Thus, LDG and ROR are highly recommended for real-time data transmission such as multimedia data transfer in CRSN

Trusted community : a novel multiagent organisation for open distributed systems

[no abstract

Towards Trustworthy, Efficient and Scalable Distributed Wireless Systems

Advances in wireless technologies have enabled distributed mobile devices to connect with each other to form distributed wireless systems. Due to the absence of infrastructure, distributed wireless systems require node cooperation in multi-hop routing. However, the openness and decentralized nature of distributed wireless systems where each node labors under a resource constraint introduces three challenges: (1) cooperation incentives that effectively encourage nodes to offer services and thwart the intentions of selfish and malicious nodes, (2) cooperation incentives that are efficient to deploy, use and maintain, and (3) routing to efficiently deliver messages with less overhead and lower delay. While most previous cooperation incentive mechanisms rely on either a reputation system or a price system, neither provides sufficiently effective cooperation incentives nor efficient resource consumption. Also, previous routing algorithms are not sufficiently efficient in terms of routing overhead or delay. In this research, we propose mechanisms to improve the trustworthiness, scalability, and efficiency of the distributed wireless systems. Regarding trustworthiness, we study previous cooperation incentives based on game theory models. We then propose an integrated system that combines a reputation system and a price system to leverage the advantages of both methods to provide trustworthy services. Analytical and simulation results show higher performance for the integrated system compared to the other two systems in terms of the effectiveness of the cooperation incentives and detection of selfish nodes. Regarding scalability in a large-scale system, we propose a hierarchical Account-aided Reputation Management system (ARM) to efficiently and effectively provide cooperation incentives with small overhead. To globally collect all node reputation information to accurately calculate node reputation information and detect abnormal reputation information with low overhead, ARM builds a hierarchical locality-aware Distributed Hash Table (DHT) infrastructure for the efficient and integrated operation of both reputation systems and price systems. Based on the DHT infrastructure, ARM can reduce the reputation management overhead in reputation and price systems. We also design a distributed reputation manager auditing protocol to detect a malicious reputation manager. The experimental results show that ARM can detect the uncooperative nodes that gain fraudulent benefits while still being considered as trustworthy in previous reputation and price systems. Also, it can effectively identify misreported, falsified, and conspiratorial information, providing accurate node reputations that truly reflect node behaviors. Regarding an efficient distributed system, we propose a social network and duration utility-based distributed multi-copy routing protocol for delay tolerant networks based on the ARM system. The routing protocol fully exploits node movement patterns in the social network to increase delivery throughput and decrease delivery delay while generating low overhead. The simulation results show that the proposed routing protocol outperforms the epidemic routing and spray and wait routing in terms of higher message delivery throughput, lower message delivery delay, lower message delivery overhead, and higher packet delivery success rate. The three components proposed in this dissertation research improve the trustworthiness, scalability, and efficiency of distributed wireless systems to meet the requirements of diversified distributed wireless applications

A consensus-based approach to reputational routing in multi-hop networks

International audienceWhen it comes to the security of the Internet of Things (IoT), securing their communications is paramount. In multi-hop networks, nodes relay information amongst themselves, opening the data up to tampering by an intermediate device. To detect and avoid such malicious entities, we grant nodes the ability to analyse their neighbours behaviour. Through the use of consensus-based validation, based upon blockchain's miners, all nodes can agree on the trustworthiness of all devices in the network. By expressing this through a node's reputation, it is possible to identify malicious devices and isolate them from network activities. By incorporating this metric into a multi-hop routing protocol such as AODV, we can influence the path selection process. Instead of defining the best route based upon overall length, we can chose the most reputable path available, thus traversing trustworthy devices. By performing extensive analyses through multiple simulated scenarios, we can identify a decrease in packet drop rates compared to AODV by ≈ 48% and ≈ 38% when subjected to black-hole attacks with 30 and 100 node networks respectively. Furthermore, by subjecting our system to varying degrees of grey-holes, we can confirm its adaptability to different types of threats

Multicloud Resource Allocation:Cooperation, Optimization and Sharing

Nowadays our daily life is not only powered by water, electricity, gas and telephony but by "cloud" as well. Big cloud vendors such as Amazon, Microsoft and Google have built large-scale centralized data centers to achieve economies of scale, on-demand resource provisioning, high resource availability and elasticity. However, those massive data centers also bring about many other problems, e.g., bandwidth bottlenecks, privacy, security, huge energy consumption, legal and physical vulnerabilities. One of the possible solutions for those problems is to employ multicloud architectures. In this thesis, our work provides research contributions to multicloud resource allocation from three perspectives of cooperation, optimization and data sharing. We address the following problems in the multicloud: how resource providers cooperate in a multicloud, how to reduce information leakage in a multicloud storage system and how to share the big data in a cost-effective way. More specifically, we make the following contributions: Cooperation in the decentralized cloud. We propose a decentralized cloud model in which a group of SDCs can cooperate with each other to improve performance. Moreover, we design a general strategy function for SDCs to evaluate the performance of cooperation based on different dimensions of resource sharing. Through extensive simulations using a realistic data center model, we show that the strategies based on reciprocity are more effective than other strategies, e.g., those using prediction based on historical data. Our results show that the reciprocity-based strategy can thrive in a heterogeneous environment with competing strategies. Multicloud optimization on information leakage. In this work, we firstly study an important information leakage problem caused by unplanned data distribution in multicloud storage services. Then, we present StoreSim, an information leakage aware storage system in multicloud. StoreSim aims to store syntactically similar data on the same cloud, thereby minimizing the user's information leakage across multiple clouds. We design an approximate algorithm to efficiently generate similarity-preserving signatures for data chunks based on MinHash and Bloom filter, and also design a function to compute the information leakage based on these signatures. Next, we present an effective storage plan generation algorithm based on clustering for distributing data chunks with minimal information leakage across multiple clouds. Finally, we evaluate our scheme using two real datasets from Wikipedia and GitHub. We show that our scheme can reduce the information leakage by up to 60% compared to unplanned placement. Furthermore, our analysis in terms of system attackability demonstrates that our scheme makes attacks on information much more complex. Smart data sharing. Moving large amounts of distributed data into the cloud or from one cloud to another can incur high costs in both time and bandwidth. The optimization on data sharing in the multicloud can be conducted from two different angles: inter-cloud scheduling and intra-cloud optimization. We first present CoShare, a P2P inspired decentralized cost effective sharing system for data replication to optimize network transfer among small data centers. Then we propose a data summarization method to reduce the total size of dataset, thereby reducing network transfer

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