The Internet of Things (IoT) bridges the gap between the physical and digital worlds, enabling seamless interaction with real-world objects via the Internet. However, IoT systems face significant challenges in ensuring efficient data generation, collection, and management, particularly due to the resource-constrained and unreliable nature of connected devices, which can lead to data loss. This paper presents DRACO (Data Replication and Collection), a framework that integrates a distributed hop-by-hop data replication approach with a routing-free mobile sink-based data collection strategy. DRACO enhances data availability, optimizes replica placement, and ensures efficient data retrieval even under node failures and varying network densities. Extensive ns-3 simulations demonstrate that DRACO outperforms state-of-the-art techniques, improving data availability by up to 15% and 34%, and replica creation by up to 18% and 40%, compared to greedy and random replication techniques, respectively. DRACO also ensures efficient data dissemination through optimized replica distribution and achieves superior data collection efficiency under varying node densities and failure scenarios as compared to commonly used uncontrolled sink mobility approaches namely random walk and self-avoiding random walk. By addressing key IoT data management challenges, DRACO offers a scalable and resilient solution well-suited for emerging use cases including industrial IoT device monitoring, smart city environmental sensing, agricultural IoT data collection, and disaster response networks, where maintaining data availability under device failures or intermittent connectivity is critical.

Energy is a fundamental component of modern life, driving nearly all aspects of daily activities. As such, the inability to access energy when needed is a significant issue that requires innovative solutions. In this paper, we propose ED-DAO, a novel fully transparent and community-driven decentralized autonomous organization (DAO) designed to facilitate energy donations. We analyze the energy donation process by exploring various approaches and categorizing them based on both the source of donated energy and funding origins. We propose a novel Hybrid Energy Donation (HED) algorithm, which enables contributions from both external and internal donors. External donations are payments sourced from entities such as charities and organizations, where energy is sourced from the utility grid and prosumers. Internal donations, on the other hand, come from peer contributors with surplus energy. HED prioritizes donations in the following sequence: peer-sourced energy (P2D), utility-grid-sourced energy (UG2D), and direct energy donations by peers (P2PD). By merging these donation approaches, the HED algorithm increases the volume of donated energy, providing a more effective means to address energy poverty. Experiments were conducted on a dataset to evaluate the effectiveness of the proposed method. The results showed that HED increased the total donated energy by at least 0.43% (64 megawatts) compared to the other algorithms (UG2D, P2D, and P2PD).

Carbon footprint reduction can be achieved through various methods, including the adoption of renewable energy sources. The installation of such sources, like photovoltaic panels, while environmentally beneficial, is cost-prohibitive for many. Those lacking photovoltaic solutions typically resort to purchasing energy from utility grids that often rely on fossil fuels. Moreover, when users produce their own energy, they may generate excess that goes unused, leading to inefficiencies. To address these challenges, this paper proposes innovative blockchain-enabled energy-sharing algorithms that allow consumers -without financial means- to access energy through the use of their own energy storage units. We explore two sharing models: a centralized method and a peer- to- peer (P2P) one. Our analysis reveals that the P2P model is more effective, enhancing the sharing process significantly compared to the centralized method. We also demonstrate that, when contrasted with traditional battery-supported trading algorithm, the P2P sharing algorithm substantially reduces wasted energy and energy purchases from the grid by 73.6%, and 12.3% respectively. The proposed system utilizes smart contracts to decentralize its structure, address the single point of failure concern, improve overall system transparency, and facilitate peer-to-peer payments.

Geographically distributed data centers provide facilities for users to fulfill the demand of storage and computations, where most of the operational cost is due to electricity consumption. In this study, we address the problem of energy consumption of cloud data centers and identify key characteristics of techniques proposed for reducing operational costs, carbon emissions, and financial penalties due to service level agreement (SLA) violations. By considering computer room air condition (CRAC) units that utilize outside air for cooling purposes as well as temperature and space-varying properties, we propose the energy cost model which takes into account temperature ranges for cooling purposes and operations of CRAC units. Then, we propose spatio-thermal-aware algorithms to manage workload using the variation of electricity price, locational outside and within the data center temperature, where the aim is to schedule the incoming workload requests with minimum SLA violations, cooling cost, and energy consumption. We analyzed the performance of our proposed algorithms and compared the experimental results with the benchmark algorithms for metrics of interest including SLA violations, cooling cost, and overall operations cost. Modeling, experiments, and verification conducted on CloudSim with realistic data center scenarios and workload traces show that the proposed algorithms result in reduced SLA violations, save between 15% to 75% of cooling cost and between 3.89% to 39% of the overall operational cost compared to the existing solutions.

The Internet of Energy (IoE) is a distributed paradigm that integrates smart networks and Internet technology. In contrast to traditional centralized energy systems, distributed Energy Internet systems with multiple components and communication requirements necessitate innovative technologies for decentralization, reliability, efficiency and security. Novel blockchain architectures, smart contracts, and distributed federated learning technologies offer new opportunities for decentralized Energy Internet services. In this paper, we classify state-of-the-art solutions utilizing blockchain, smart contracts, and federated learning for the IoE domains. In this context, four representative system models are identified and discussed. These models demonstrate various ways in which blockchain, smart contracts, and federated learning can be integrated to support the main domains of IoE, namely distributed energy trading and sharing, smart microgrid energy networks, and electric and connected vehicle management.

Various resources are provided by cloud computing over the Internet, which enable plenty of applications to be employed to offer different services for industries. However, cloud computing due to the relying on a central server/datacenter has limitations such as high latency and response time, which are so crucial in real time applications like healthcare systems. To solve this, edge computing paradigm paves the way and provides pioneering solutions by moving the computational and storage resources closer to the end users. Edge computing by facilitating the real-time applications becomes more suitable for healthcare systems. This paper uses edge technology for detecting heart disease in patients utilizing a hybrid machine learning method. Although there exist some works in this area, there is still a need for improving the prediction accuracy. To this end, this paper proposes a meta-heuristic-based feature selection method using Black Widow Optimization (BWO) algorithm, and then, applies different classifiers on the selected features. The experimental results show that AdaBoost classifier along with BWO-based feature selection by 90.11 % accuracy outperforms other experimental methods, such as KNN, SVM, DT, and RF.

With the rapid growth of edge-assisted solutions in Internet of Things (IoT) networks, connected healthcare progressively relies on such solutions. This refers to systems in which all the healthcare stakeholders are connected to each other. These systems employ novel technologies, such as IoT, edge computing, and artificial intelligence (AI) to convert conventional health systems to more effective, appropriate, and customized intelligent systems. However, such systems encounter many restrictions and require new policies. By moving the computation and processing closer to the data sources and end-users, fog becomes edge computing which can reduce latency, bandwidth usage, and energy consumption. To the best of our knowledge, there is no systematic and methodological research in this scope that investigates the existing studies considering various vital and relevant factors. Thus, this survey aims to examine the state-of-the-art research in this area. We have reviewed a significant number of papers in this area and divided them into two main taxonomies, patient-centric and process-centric techniques. Furthermore, essential factors, such as available data sets and parameters like accuracy, mobility, and data rates are described and examined. Our aim is to bridge the gap between edge computing and connected healthcare solutions by discussing the challenges and highlighting future trends.

Abstract Growing intelligent cities is witnessing an increasing amount of local energy generation through renewable energy resources. Energy trade among the local energy generators (aka prosumers) and consumers can reduce the energy consumption cost and also reduce the dependency on conventional energy resources, not to mention the environmental, economic, and societal benefits. However, these local energy sources might not be enough to fulfill energy consumption demands. A hybrid approach, where consumers can buy energy from both prosumers (that generate energy) and also from prosumer of other locations, is essential. A centralized system can be used to manage this energy trading that faces several security issues and increase centralized development cost. In this paper, a hybrid energy trading system coupled with a smart contract named SynergyGrids has been proposed as a solution, that reduces the average cost of energy and load over the utility grids. To the best of our knowledge, this work is the first attempt to create a hybrid energy trading platform over the smart contract for energy demand prediction. An hourly energy data set has been utilized for testing and validation purposes. The trading system shows 17.8% decrease in energy cost for consumers and 76.4% decrease in load over utility grids when compared with its counterparts.

As a distributed hash table (DHT) routing overlay, Skip Graph is used in a variety of peer-to-peer (P2P) systems including cloud storage. The overlay connectivity of P2P systems is negatively affected by the arrivals and departures of nodes to and from the system that is known as churn. Preserving connectivity of the overlay network (i.e., the reachability of every pair of nodes) under churn without compromising the overlay latency is a performance challenge in every P2P system including the Skip Graph-based ones. The existing decentralized churn stabilization solutions that are applicable to Skip Graphs mainly optimize the connectivity of the system under churn and do not consider routing latency of overlay as an optimization goal. Additionally, those existing solutions change the message complexity of Skip Graphs, distort its topology, or apply constant message overhead to the system. In this paper, we propose Interlaced , a fully decentralized churn stabilization mechanism for Skip Graphs that provides drastically stronger overlay connectivity and faster search queries without changing the asymptotic complexity of the Skip Graph in terms of storage, computation, and communication. We also propose the Sliding Window De Bruijn Graph (SWDBG ) as a tool to predict the availability of nodes with high accuracy. Our simulation results show that in comparison to the best existing DHT-based solutions, Interlaced improves the overlay connectivity of the Skip Graph under churn with the gain of about 1.73 times. Likewise, compared to the existing availability prediction approaches for P2P systems, SWDBG is about 1.26 times more accurate. A Skip Graph that benefits from Interlaced and SWDBG is about 2.47 times faster on average in routing the queries under churn compared to the best existing solutions. We also present an adaptive extension of Interlaced to be applied to other DHTs, for example, Kademlia.

Industrial investments into distributed energy resource technologies are increasing and playing a pivotal role in the global transactive energy, as part of a wider drive to provide a clean and stable source of energy. The management of prosumers, which consume and as well as generate energy, with heterogeneous energy sources is critical for sustainable and efficient energy trading procedures. This article proposes a blockchain-assisted adaptive model, namely SynergyChain, for improving the scalability and decentralization of the prosumer grouping mechanism in the context of peer-to-peer energy trading. Smart contracts are used for storing the transaction information and for the creation of the prosumer groups. SynergyChain integrates a reinforcement learning module to further improve the overall system performance and profitability by creating a self-adaptive grouping technique. The proposed SynergyChain is developed using Python and Solidity and has been tested using Ethereum test nets. The comprehensive analysis using the hourly energy consumption dataset shows a 39.7% improvement in the performance and scalability of the system as compared to the centralized systems. The evaluation results confirm that SynergyChain can reduce the request completion time along with an 18.3% improvement in the overall profitability of the system as compared to its counterparts.

As an append-only distributed database, blockchain is utilized in a vast variety of applications including the cryptocurrency and Internet-of-Things (IoT). The existing blockchain solutions show downsides in communication and storage scalability, as well as decentralization. In this article, we propose LightChain, which is the first blockchain architecture that operates over a Distributed Hash Table (DHT) of participating peers. LightChain is a permissionless blockchain that provides addressable blocks and transactions within the network, which makes them efficiently accessible by all peers. Each block and transaction is replicated within the DHT of peers and is retrieved in an on-demand manner. Hence, peers in LightChain are not required to retrieve or keep the entire ledger. LightChain is fair as all of the participating peers have a uniform chance of being involved in the consensus regardless of their influence such as hashing power or stake. We provide formal mathematical analysis and experimental results (simulations and cloud deployment) to demonstrate the security, efficiency, and fairness of LightChain, and show that LightChain is the only existing blockchain that can provide integrity under the corrupted majority power of peers. As we experimentally demonstrate, compared to the mainstream blockchains such as Bitcoin and Ethereum, LightChain requires around 66 times smaller per node storage, and is around 380 times faster on bootstrapping a new node to the system, and each LightChain node is rewarded equally likely for participating in the protocol.

Software Defined Networking (SDN) has eased the management and control of networks through separation of the control and data planes. Software defined data centers (SDDC) automate the management of end systems which are physical machines and virtual machines. In data centers, although there is a vast work on minimizing power consumption of physical machines and virtual machine migration performance, energy efficiency of the network components is given little attention. In this paper, a software-based energy efficiency framework that jointly minimizes the power consumption of end systems and network components in SDDC is proposed. Moreover, a novel physical server utility interval based metric, namely Ratio for Energy Saving of Physical Machines (RESPM) which measures how energy efficient the physical servers with respect to virtual machines residing within is proposed. To jointly maximize network energy efficiency and RESPM values, an Integer Programming (IP) formulation has been introduced. Experiments conducted on real-world virtual migration traces show that the proposed framework jointly reduces the power consumption of end systems and network components. The system has shown an improvement of 9% in RESPM, 35% energy saving in Ratio of Energy Saving in SDN (RESDN), and more than 50% in links saving.

As a decentralized immutable ledger where several trustless peers can reach consensus with each other without the need of any trusted third party, blockchain technology fits perfectly with the peer-to-peer (P2P) energy trading paradigm. In this paper, we propose, design and analyze a marketplace for energy trading based on smart contracts on the blockchain. The proposed system named Joulin serves as a competitive and efficient marketplace where peers can both produce, buy and sell energy depending on their needs. As a proof-of-concept, we developed the prototype of the Joulin system using Ethereum blockchain. Our results, in terms of usability, flexibility and resiliency, demonstrate the potential to achieve an easily extendable and reliable system with low transaction costs. Low Ethereum gas costs and quick response times demonstrate usability. Our smart contracts have also been tested with security tools to ensure that they are not vulnerable to outside manipulations.

With improvements in computation and storage resources, data access through the network becomes the bottleneck for several cloud applications. Even with high-speed networks, the high latency of the cloud access makes it unfeasible or un-favourable for latency-sensitive applications such as autonomous driving, smart factories, and video streaming. Edge computing provides a solution by utilizing the network edge resources that are closer to the end users. Nevertheless, it is a non-trivial task to design a large-scale edge-capable system that is stable, fault-tolerant, and efficient [1]. In this paper, present the design of EdgeKV: a novel general-purpose distributed key-value store for the network edge. We demonstrate the features of EdgeKV for achieving high efficiency and scalability while providing flexibility, ease of use, and data privacy. We evaluated our prototype on the Grid'5000 framework with multiple realistic Yahoo! Cloud Serving Benchmark (YCSB) workloads. Our initial results show that EdgeKV achieves 72% higher throughput and 47% lower latency on average than centralized cloud storage, for read-dominated workloads.

Information Centric Networks (ICNs) have emerged in recent years in order to provide effective mechanisms for content distribution and retrieval. However, these future network architectures are vulnerable to Distributed Denial of Service Attacks (DDoS). In this paper, we propose an effective mechanism to counter these attacks in path identifier-based information centric networks. The proposed mechanism, called GET Message logging based filtering (GMLF), employs Bloom filter based logging to store incoming GET messages such that corresponding content messages are verified, while filtering packets originating from malicious hosts.

Skip Graphs belong to the family of Distributed Hash Table (DHT) structures that are utilized as routing overlays in various peer-to-peer applications including blockchains, cloud storage, and social networks. In a Skip Graph overlay, any misbehavior of peers during the routing of a query compromises the system functionality. Guard is the first authenticated search mechanism for Skip Graphs, enables reliable search operation in a fully decentralized manner. In this demo paper, we present a proof-of-concept implementation of Guard on Skip Graph nodes as well as a deployment demo scenario.

Skip Graphs are Distributed Hash Table (DHT)based data structures that are immensely utilized as routing overlays in Peer-to-Peer (P2P) applications. In this demo paper, we present the software architecture of our open-source implementation of Skip Graph middleware in Java. We also present a demo scenario on configuration and constructing an overlay of Skip Graph processes in a fully decentralized manner. Our implementation is capable of hosting data objects at the Skip Graph processes and serving as a P2P data storage platform as well. Our middleware implementation provides an open-source platform to support Skip Graph-based applications on top of it.

Energy trading systems have revolutionized by taking advantage of energy users who produce surplusenergy. In the cyberphysical energy sharing systems, the participation of such consumers who can also sell their residuum energy for profit, namely prosumers, is critical for the sustainable and efficient energy sharing procedure and requires improved prosumer management. The idea of grouping the prosumers for better profits is a promising approach for prosumer management which is currently carried out in centralized manner; that face trust, security and scalability issues. Hence, a strong tool that can protect the prosumer privacy; log the changes for audit purposes and eventually improve the performance of the system is necessary. This paper proposes a blockchain-assisted approach using smart contracts for improved scalability and decentralization of the prosumer grouping mechanism in the context of P2P energy trading. The results show around 38.7% improvement in the performance and scalability of the system.

Online Social Networks (OSNs) offer free storage and social networking services through which users can communicate personal information with one another. The personal information of the users collected by the OSN provider comes with privacy problems when being monetized for advertising purposes. To protect user privacy, existing studies propose utilizing data encryption that immediately prevents OSNs from monetizing users data and hence leaves secure OSNs with no convincing commercial model. To address this problem, we propose Privado as a privacy-preserving group-based advertising mechanism to be integrated into secure OSNs to re-empower monetizing ability. Privado is run by N servers, each provided by an independent provider. User privacy is protected against an active malicious adversary controlling N − 1 providers, all the advertisers, and a large fraction of the users. We base our design on the group-based advertising notion to protect user privacy, which is not possible in the personalized variant. Our design also delivers advertising transparency; the procedure of identifying target customers is operated solely by the OSN servers without getting users and advertisers involved. We carry out experiments to examine the advertising running time under various number of servers and group sizes. We also argue about the optimum number of servers with respect to user privacy and advertising running time.

Software-defined networking (SDN) paradigm, with the flexible and logically centralized control, enables dynamically minimizing the network energy consumption by redirecting paths of packets. However, the links and switches are designed to accommodate maximum traffic volume and their power consumption is not traffic proportional. Moreover, there exists a trade-off between energy efficiency and network performance that need to be considered together. Addressing these issues, we propose an energy efficiency metric named Ratio for Energy Saving in SDN (RESDN) that quantifies energy efficiency based on link utility intervals. We provide integer programming formulation and method for maximizing the RESDN of the network. To the best of our knowledge, RESDN approach is novel as it measures how links are profitably utilized in terms of the amount of energy they consume with respect to their utility. We analyze our approach considering various metrics of interest and different types of SDN enabled switches. Experiments show that maximizing the RESDN value improves energy efficiency while maintaining acceptable network performance. In comparison to state-of-the-art utility-based heuristics, RESDN method achieves up to 30% better ratio for energy saving, 14.7 watts per switch power saving, 38% link saving, 2 hops decrease in average path length, and 5% improved traffic proportionality.

As a Distributed Hash Table (DHT), Skip Graph routing overlays are exploited in several peer-to-peer (P2P) services, including P2P cloud storage. The fully decentralized replication algorithms that are applicable to the Skip Graph-based P2P cloud storage fail on improving the performance of the system with respect to both the availability of replicas as well as their response time. Additionally, they presume the system as homogeneous with respect to the nodes' latency distribution, availability behavior, and bandwidth, or storage. In this article, we propose Pyramid, which is the first fully decentralized utility- and locality-aware replication approach for Skip Graph-based P2P cloud storage systems. Pyramid considers the nodes as heterogeneous with respect to their latency distribution, availability behavior, bandwidth, and storage. Pyramid is utility-aware as it maximizes the average available bandwidth of replicas per time slot (e.g., per hour). Additionally, Pyramid is locality-aware as it minimizes the average latency between nodes and their closest replica. Our simulation results show that compared to the state-of-the-art solutions that either perform good in utility-awareness, or in locality-awareness, our proposed Pyramid improves both the utility- and locality-awareness of replicas with a gain of about 1.2 and 1.1 times at the same time, respectively.

Software defined networking (SDN) is a rapidly growing networking paradigm in both industry and research areas, with network programmability as its powerful feature which enables propagating changes in the network easily. However, the links and switches are designed to accommodate maximum traffic load and their power consumption is not traffic aware. The logically centralized control in SDN enables dynamically minimizing the energy consumption of the links and the switches by diverting paths of packets. Energy efficiency and performance are opposite objectives that have to be addressed simultaneously. As the main contributions in this study, we first propose an energy efficiency metric Energy Profit Threshold (EPT) that is applicable to SDN. Then, we provide Integer Programming (IP) formulation with the objective of maximizing the EPT of a software defined network environment. Experimental results show that maximizing the EPT value exhibits energy saving of more than 35 % as compared to other utility based energy saving algorithms.

Several cloud computing applications use Replicated State Machines (RSM) to provide fault-tolerant services, ensuring consistency with consensus protocols. However, these protocols often come with a high latency cost, sometimes even forcing system designers to sacrifice consistency for availability. This latency is due, in part, to unnecessary data copies in the kernel TCP/IP layers. Remote Direct Memory Access (RDMA) bypasses the kernel to provide faster communication and lower CPU overhead through zero-copy data transfer. Recent works have utilized RDMA primitives to improve the performance of consensus protocols. However, integrating RDMA into such protocols and utilizing it efficiently can be a complex task. In this paper, we address this problem by presenting a systematic review of the state-of-the-art approaches for implementing RDMA-based consensus protocols.

Summary Several distributed services ranging from key‐value stores to cloud storage require fault‐tolerance and reliability features. For enabling fast recovery and seamless transition, primary‐backup replication protocols are widely used in different application settings including distributed databases, web services, and the Internet of Things. In this study, we elaborate the ways of enhancing the efficiency of the primary‐backup replication protocol by introducing various checkpointing techniques. We develop a geographically replicated key‐value store based on the RocksDB and use the PlanetLab testbed network for large‐scale performance analysis. Using various metrics of interest including blocking time, checkpointing time, checkpoint size, failover time, and throughput and testing with practical workloads via the YCSB tool, our findings indicate that periodic‐incremental checkpointing promises up to 5 times decrease in blocking time and a drastic improvement on the overall throughput compared to the traditional primary‐backup replication. Furthermore, enabling Snappy compression algorithm on the periodic‐incremental checkpointing leads to further reduction in blocking time and increases system throughput compared to the traditional primary‐backup replication.

Replication is useful for supporting fault-tolerance, reliable and recovery oriented distributed systems. Popular application areas include databases, P2P systems, web services and Internet of Things. In this study, we propose utilizing the checkpointing concept for improving the efficiency of the well-known primary-backup replication protocol in distributed systems. We developed a software framework based on an in-memory replicated key-value store to evaluate various checkpointing algorithms. Using the framework over geographically distributed nodes of the PlanetLab platform, we performed extensive experiments and analysis with several different metrics, including blocking time, checkpointing time, checkpoint size and recovery time. Experimental scenarios consist of using the well-known benchmarking tool, YCSB, performing realistic read/update queries through exemplary workloads. Our findings indicate that incremental checkpointing combined with a periodic usage is the most efficient approach with having up to 30-times better system throughput and 50% decrease in average blocking times compared to traditional primary-backup replication and other checkpointing algorithms.

The prominent cloud services rely on geographically distributed nodes running replication and other fault-tolerance mechanisms so as to provide flawless availability and dependability. In this paper, we address the communication cost of the well known primary-backup replication protocol, and propose compressed periodic incremental checkpoint algorithms to achieve improved throughput. We set up a replicated key-value store on geographically distributed nodes of the PlanetLab platform, and developed compressed incremental checkpointing algorithms to support primary-backup replication. By considering performance metrics of interest including blocking time, checkpointing time, compression ratio, compression/ decompression times, we conducted a comprehensive analysis. We used the well-known benchmarking tool YCSB and established different sample workloads to test where each workload represents diverse plots. Our findings indicate that Zstd is the most competent compression method under all scenarios and through comparing with an uncompressed approach we point out that compressing the communication data disseminated from the primary replica coupled with the periodic incremental checkpointing algorithm not only decreases the average blocking time up to 5% but it also improves the overall system throughput by 4% compared to the no compression case.

Servers in data centers consume large amount of energy which increase the operational cost for cloud service providers, that spend a major portion of their revenue to pay bills due to inefficient workload assignment and wastage of resources. In order to minimize the operational cost of data centers, it is essential to optimize the scheduling of the jobs. In this paper, we address the problem of inefficient cooling system, SLA violations due to network delays and processing delays in geographically distributed data centers. We propose scheduling algorithms that aim to minimize the cooling cost by exploiting the temperature variations within the data centers and electricity cost by taking advantage of time-space-varying fluctuation of electricity prices. SLA violations are aimed to be minimized by assigning jobs considering deadlines, network delays and queuing delays. Experiments conducted on CloudSim show that price/cooling aware and delay sensitive scheduling reduces the overall cost by 22% as compared to random scheduling.

Skip Graph, a member of the distributed hash table (DHT) family, has several benefits as an underlying structure in peer-to-peer (P2P) storage systems. In such systems, replication plays a key role on the system's performance. The traditional decentralized replication algorithms do not consider the locations of Skip Graph nodes in the network. Negligence of node locations in the placement of the replicas results in high access delays between the nodes and their closest replicas. This negatively affects the performance of the whole storage system. In this paper, with the aim of making Skip Graph's replication locality aware, we propose dynamic fully decentralized LARAS approach, where the data owner can replicate itself based on the system size, possible data requester nodes' set and using local information of the storage system. Our extensive performance results show that LARAS improves replication access delay of the Skip Graph based storage system about 20% and 38% in comparison to the best known decentralized counterpart in the public and private replication scenarios, respectively.

The automotive industry is under a major change and new vehicles are being enriched by the recent advances in communication. Not only business plans are changing due to connected and urbanized lifestyle, but also transportation is becoming more intelligent with smart roads that connect smart cars. Technology coined as the vehicular ad-hoc network (VANET) is harmonizing with Intelligent Transportation System (ITS) and Intelligent Traffic System (ITF). However, ITS and ITF systems suffer from the scarcity of radio frequency spectrum. Visible light communication (VLC) that uses modulated optical radiation in the visible light spectrum is an alternative medium being researched. To date, the majority of research on vehicular VLC was aimed at achieving high data rates provided that high lighting quality is achieved without any concern on dimmable LED lights. Auto-dimmable headlights gain attention due to danger caused by sudden glare on drivers at night conditions which makes dimming in VLC necessary. In this paper, we first present the latest concept of vehicular VLC on ITS and ITF systems and address dimming utility. We then demonstrate experimentally that dimming is a key parameter in VLC which affects data dissemination and received power signal strength.

With increasing popularity of cloud storage, efficiently proving the integrity of data stored on an untrusted server has become significant. Authenticated skip lists and rank-based authenticated skip lists (RBASL) have been used to provide support for provable data update operations in cloud storage. However, in a dynamic file scenario, an RBASL based on block indices falls short when updates are not proportional to a fixed block size; such an update to the file, even if small, may result in O ( n ) updates on the data structure for a file with n blocks. To overcome this problem, we introduce FlexList, a flexible length-based authenticated skip list. FlexList translates variable-size updates to O (⌈ u/B ⌉) insertions, removals, or modifications, where u is the size of the update and B is the (average) block size. We further present various optimizations on the four types of skip lists (regular, authenticated, rank-based authenticated, and FlexList). We build such a structure in O ( n ) time and parallelize this operation for the first time. We compute one single proof to answer multiple (non)membership queries and obtain efficiency gains of 35%, 35%, and 40% in terms of proof time, energy, and size, respectively. We propose a method of handling multiple updates at once, achieving efficiency gains of up to 60% at the server side and 90% at the client side. We also deployed our implementation of FlexDPDP (dynamic provable data possession (DPDP) with FlexList instead of RBASL) on PlanetLab, demonstrating that FlexDPDP performs comparable to the most efficient static storage scheme (provable data possession (PDP)) while providing dynamic data support.

Energy efficiency in P2P systems has become a prominent issue due to significant portions of the Internet traffic devoted to P2P protocols. In this paper, we address the approaches for energy efficiency in BitTorrent Protocol. First, we provide a classification of the existing energy efficient BitTorrent studies, and then propose multi-proxy and private proxy approaches to minimize energy consumption. We develop energy cost formulations for our proposed protocols as well as the legacy BitTorrent. The performance of multi-proxy and private proxy BitTorrent is analyzed through a comprehensive set of simulations performed on Peersim for the energy consumption and average download time metrics. For the multi-proxy protocol, we investigate the effect of increasing the number of proxies on the overlay. We also present preliminary experimental results on large-scale scenarios revealing that the proxy-based schemes reduce the energy consumption up to 80% without any performance degradation in comparison to the legacy BitTorrent.

Quality of user experience in Distributed Interactive Applications(DIAs) highly depends on the network latencies during the system execution. In DIAs, each user is assigned to a server and communication with any other client is performed through its assigned server. Hence, latency measured between two clients, called interaction time, consists of two components. One is the latency between the client and its assigned server, and the other is the inter-server latency, that is the latency between servers that the clients are assigned. In this paper, we investigate a real-time client to server assignment scheme in a DIA where the objective is to minimize the interaction time among clients. The client assignment problem is known to be NP-complete and heuristics play an important role in finding near optimal solutions. We propose two distributed heuristic algorithms to the online client assignment problem in a dynamic DIA system. We utilized real-time Internet latency data on the Planet Lab platform and performed extensive experiments using geographically distributed Planet Lab nodes where nodes can arbitrarily join/leave the system. The experimental results demonstrate that our proposed algorithms can reduce the maximum interaction time among clients up to 45% compared to an existing baseline technique.

Developing real-time safety and non-safety applications for vehicular ad hoc networks (VANET) requires understanding the dynamics of the network topology characteristics since these dynamics determine both the performance of routing protocols and the feasibility of an application over VANET. Using various key metrics of interest including node degree, number of clusters, link duration and link quality, we provide a realistic analysis of the VANET topology characteristics. In this analysis, we integrate real-world road topology and real-time data extracted from Freeway Performance Measurement System database into the microscopic mobility model in order to generate realistic traffic flows along the highway. Moreover, we use more realistic, recently proposed, obstacle-based channel model and compare the performance of this sophisticated model to the most commonly used more simplistic channel models including unit disc and log-normal shadowing model. Our investigation on the key system metrics reveal that largely used unit disc model fails to realistically model communication channel, while parameters of simplistic models like log normal can be adjusted to match the corresponding system metrics of more complex and hard to implement obstacle based model.

Clustering is an effective mechanism to handle the fast changes in the topology of vehicular ad hoc networks (VANET) by using local coordination. Constructing stable clusters by determining the vehicles sharing similar mobility pattern is essential in reducing the overhead of clustering algorithms. In this paper, we introduce VMaSC: Vehicular Multi-hop algorithm for Stable Clustering. VMaSC is a novel clustering technique based on choosing the node with the least mobility calculated as a function of the speed difference between neighboring nodes as the cluster head through multiple hops. Extensive simulation experiments performed using ns-3 with the vehicle mobility input from the Simulation of Urban Mobility (SUMO) demonstrate that novel metric used in the evaluation of the least mobile node and multi-hop clustering increases cluster head duration by 25% while decreasing the number of cluster head changes by 10%.

We consider buffer management problem in support of large-scale bio-inspired peer-to-peer data dissemination services. Bio-inspired epidemic protocols have considerable benefits as they are robust against network failures, scalable and provide probabilistic reliability guarantees. Coupled with an efficient buffering mechanism, system wide buffer usage can be optimized while providing reliability and scalability in such protocols. We propose a novel algorithm, Stepwise Fair-share Buffering, that provides uniform load distribution in comparison to earlier approaches and reduces the overall buffer usage where every peer has the partial view of the system. We report and discuss the comparative performance results and provide an analytical evaluation of our approach.

We present a novel protocol EraMobile (epidemic-based reliable and adaptive multicast for mobile ad hoc networks) and its performance analysis. EraMobile's target is group applications requiring high-level of reliability and the protocol aims to provide fully reliable multicast data delivery with minimal network overhead even in the adverse network conditions. EraMobile utilizes an epidemic-based method in multicast operation to cope with dynamic and unpredictable topology changes arising from the mobility. Our epidemic mechanism does not require the maintenance of any tree- or mesh-like structure for multicasting. It also needs neither having global or partial view of the network nor having information of neighboring nodes and group members. Besides, it substantially minimizes the overhead incurred by eliminating redundant data transmissions. Another distinguishing feature of EraMobile is its capability of adapting to varying node densities in order to provide reliable data delivery in both sparse networks, where the network connectivity is prone to interruptions, and dense networks, where congestion is likely to occur. We study the performance of EraMobile through comparative and extensive simulations on ns-2 network simulator. EraMobile is shown to achieve fully reliable multicasting for most of the scenarios investigated.

We compare pull and push-based epidemic paradigms for information diffusion in large scale networks. Key benefits of these approaches are that they are fully distributed, utilize local information only via pair-wise interactions, and provide eventual consistency, scalability and communication topology-independence, which make them suitable for peer-to-peer distributed systems. We develop a chain-Binomial epidemic probability model for these algorithms. Our main contribution is the exact computation of message delivery latency observed by each peer, which corresponds to a first passage time of the underlying Markov chain. Such an analytical tool facilitates the comparison of pull and push-based spread for different group sizes, initial number of infectious peers and fan-out values which are also accomplished in this study. Via our analytical stochastic model, we show that push-based approach is expected to facilitate faster information spread both for the whole group and as experienced by each member.

A new peer-to-peer architecture for multipoint video conferencing that targets end points with low bandwidth network connections (single video in and out) is presented. It enables end points to create a multipoint conference without any additional networking and computing resources than what is needed for a point-to-point conference. The new architecture is based on layered video coding with two layers at the end points. It allows each conference participant to see any other participant at any given time under all multipoint configurations of any number of users, with a caveat that some participants may have to receive only the base layer video. Layered encoding techniques usable within this architecture are described. A protocol for implementation of the new approach has been developed and simulated. Its performance is analyzed.

There are many methods for making a multicast protocol “reliable.” At one end of the spectrum, a reliable multicast protocol might offer tomicity guarantees, such as all-or-nothing delivery, delivery ordering, and perhaps additional properties such as virtually synchronous addressing. At the other are protocols that use local repair to overcome transient packet loss in the network, offering “best effort” reliability. Yet none of this prior work has treated stability of multicast delivery as a basic reliability property, such as might be needed in an internet radio, television, or conferencing application. This article looks at reliability with a new goal: development of a multicast protocol which is reliable in a sense that can be rigorously quantified and includes throughput stability guarantees. We characterize this new protocol as a “bimodal multicast” in reference to its reliability model, which corresponds to a family of bimodal probability distributions. Here, we introduce the protocol, provide a theoretical analysis of its behavior, review experimental results, and discuss some candidate applications. These confirm that bimodal multicast is reliable, scalable, and that the protocol provides remarkably stable delivery throughput.