Distributed Storage Coding for Flexible and Efficient Data Dissemination and Retrieval in Wireless Sensor Networks

Online social networks (OSNs) such as Facebook and Google+ have transformed the way our society communicates. However, this success has come at the cost of user privacy; in today's OSNs, users are not in control of their own data, and depend on OSN operators to enforce access control policies. A multitude of privacy breaches has spurred research into privacy-preserving alternatives for social networking, exploring a number of techniques for storing, disseminating, and controlling access to data in a decentralized fashion. In this paper, we argue that a combination of techniques is necessary to efficiently support the complex functionality requirements of OSNs.We propose Cachet, an architecture that provides strong security and privacy guarantees while preserving the main functionality of online social networks. In particular, Cachet protects the confidentiality, integrity and availability of user content, as well as the privacy of user relationships. Cachet uses a distributed pool of nodes to store user data and ensure availability. Storage nodes in Cachet are untrusted; we leverage cryptographic techniques such as attribute based encryption to protect the confidentiality of data. For efficient dissemination and retrieval of data, Cachet uses a hybrid structured-unstructured overlay paradigm in which a conventional distributed hash table is augmented with social links between users. Social contacts in our system act as caches to store recent updates in the social network, and help reduce the cryptographic as well as the communication overhead in the network.We built a prototype implementation of Cachet in the FreePastry simulator. To demonstrate the functionality of existing OSNs we implemented the newsfeed application. Our evaluation demonstrates that (a) decentralized architectures for privacy preserving social networking are feasible, and (b) use of social contacts for object caching results in significant performance improvements.

Security and efficient data dissemination over wireless network has been the focus of research in academic, military and commercial domains for many years. Unfortunately, proposed security solutions for data dissemination over wireless network are not efficient enough. In this work, a novel security and efficient data dissemination scheme which integrates authentication into a SE-Raptor (security enhanced Raptor) codes is investigated. In SE-Raptor codes, encoding packets with degree one are encoded again, and many promiscuous packets are took part in encoding. Simulation results show that the proposed method has better performance in terms of encoding ratio, degree ratio, encoding and decoding efficiency. Additionally, our analysis shows that data confidentiality, bogus data protection and deny of service properties are provided by the proposed scheme.

The goal of this paper is to design and implement an efficient data dissemination algorithm for wireless sensor networks. The proposed approach requires nodes in a network to publish their characteristics to their neighboring nodes. Each node selects its parent node for data forwarding using a method similar to natural selection. Fitness score for selection is locally calculated considering several characteristics of a neighboring node such as: battery level, distance, and hierarchy level. The main goal of this algorithm is to extend the wireless sensor network lifetime by distributing the traffic load among the nodes that have higher probabilities of predicted survival, which is identified by their fitness score. In addition, this approach can provide a framework to study the emergent behaviors of networks when different local rules for node selection are applied.

Wireless Sensor Networks (WSNs) are popular in smart homes to implement context-aware and intelligent services. Due to fixing bugs and adding new functions, the software on sensor nodes needs to be updated depending on efficient bulk data dissemination. Existing page transfer of data dissemination protocols employ pipelining technique to achieve spatial multiplexing. The conventional pipelining inefficiency, however, becomes a bottleneck in terms of dissemination delay and communication overhead. In this paper, we propose SAPM, a novel Self-Adjusting Pipelining Mechanism for efficient bulk data dissemination. By considering the impact of link correlation, detecting accumulated pages in sensor nodes, and carefully selecting the number of pages to request, SAPM shortens the dissemination delay effectively and reduces communication overhead to improve current bulk data dissemination protocols' performance. Our evaluation results on test bed experiments and extensive simulations show that SAPM reduces the completion time of state-of-the-art bulk data dissemination protocols by 27.8% on average compared to naive solution and the number of transmissions by 17.4% on average under high link correlation while having a low memory overhead.

Data retrieval problem is a novel research direction about wireless data broadcast. The goal is to find an efficient sequence to download the requested information from public information in wireless communication. The types of data items broadcast in channel are followed: consecutive and no replicated, non-consecutive and no replicated, and nonconsecutive and replicated. The goal of above types is to reduce the energy consumption, but the complexity is introduced. In order to reduce the complexity of wireless data broadcast with non-consecutive and replicated data items, we define MIMO parallel data retrieval problem with replicated data items and prove that it is NP-hard. In this paper, we also design two heuristic algorithms for data retrieval problem in MIMO and MISO wireless data broadcast. The experiment results show that the proposed schemes have better performance in the case of less requested data items.

Bulk data dissemination is a basic building block for enabling software update and reprogramming in wireless sensor networks. The recent structure based approach looks promising for efficient dissemination since it facilitates transmission and sleep scheduling. However, a number of limitations exist in existing structured protocols. In this paper, we propose a correlated core based solution for efficient bulk data dissemination in wireless sensor networks. We propose an efficient backbone node selection algorithm to construct the core structure by exploiting link correlation. We also design a novel negotiation mechanism which greatly reduces the control message overhead as compared to the existing structured protocols. We conduct both simulation and testbed experiments, and the results show that our proposed solution outperforms the state-of-the-art in terms of both the number of transmissions and the completion time.

The advantages of deploying mobile sinks (MSs) in wireless sensor networks (WSNs) have attracted tremendous attention recently, because mobility of a sink provides an efficient method to improve data gathering performance in WSNs. However, in such kind of WSN, the efficiency of data dissemination is obviously affected by the trajectory of the MS. Therefore, optimal moving schemes of the MS have been studied in many researches. Nevertheless, in many cases, the trajectory of the MS is not able to change ideally as previous studies assumed but depending on the environmental conditions. In this paper, we focus on the efficiency of data gathering in WSN with a MS whose trajectory cannot change ideally and propose a new data diffusion scheme - implementing a new Pre-Diffusion Algorithm (PDA) in a hierarchical WSN aimed at improving data gathering efficiency. This hierarchical WSN is composed of several clusters, and datatransmission is done by higher powered cluster head (CH) node. Our scheme predetermines the cluster area where the MS is going to arrive in most probability in next period of time. MS fulfills data converging in the cluster head (CH)whose distance to the MS may the smallest compared with other CHs when data gathering starts. Thus more data can be collected with less trajectory length of MS. Simulation results show that our proposed scheme improves data gathering efficiency as compared to conventional scheme.

With the fast development of industrial Internet of things (IIoT), a large amount of data is being generated continuously by different sources. Storing all the raw data in the IIoT devices locally is unwise considering that the end devices’ energy and storage spaces are strictly limited. In addition, the devices are unreliable and vulnerable to many threats because the networks may be deployed in remote and unattended areas. In this paper, we discuss the emerging challenges in the aspects of data processing, secure data storage, efficient data retrieval and dynamic data collection in IIoT. Then, we design a flexible and economical framework to solve the problems above by integrating the fog computing and cloud computing. Based on the time latency requirements, the collected data are processed and stored by the edge server or the cloud server. Specifically, all the raw data are first preprocessed by the edge server and then the time-sensitive data (e.g., control information) are used and stored locally. The non-time-sensitive data (e.g., monitored data) are transmitted to the cloud server to support data retrieval and mining in the future. A series of experiments and simulation are conducted to evaluate the performance of our scheme. The results illustrate that the proposed framework can greatly improve the efficiency and security of data storage and retrieval in IIoT.

In large-scale Wireless Sensor Networks (WSNs) the amount of data gathered require energy efficient data dissemination and data retrieval techniques. Data Centric Sensor (DCS) networks is a better approach in which the sensed data are sent to a sensor node whose name is associated with sensed data. Due to unattended nature of Wireless Sensor Networks, these sensor nodes are susceptible to different types of attacks. In this paper we propose a Secure Data Centric Sensor (SDCS) Networks that includes security and privacy support to DCS networks. In addition, we propose a multi-query optimization technique that aggregates similar queries and reduces the number of messages. Simulation and experimental results show that our work provides a secure data centric sensor network based on cryptographic keys and reduces the message overhead and incurs a minimum communication cost compared to previous works.

Given a set of multiple requests sent by clients, where each request contains multiple data items, multi-antenna data retrieval problem refers that finds an access pattern (to retrieve multiple requests by using multiple antennae) such that the access latency of each antenna is minimized and the total access latency in all antennae keeps balance. The problem has great value in mobile computing applications. Although almost researches have focused on data retrieval problem when the clients equipped with one antenna send single request and multiple requests, there are few studies on data retrieval problem with multiple requests sent by clients equipped with multiple antennae. Therefore, this paper proposes two algorithms that adopt two different grouping schemes (SG and WG) such that the requests can be reasonably retrieved by each antenna. For retrieving each request, we propose an algorithm that converts wireless data broadcast system to a super tree for finding an access pattern to download all requested data items in minimal access latency. Through experiments, the proposed scheme has currently most efficiency in existing schemes.

Fountain codes are linear codes with low complexities. LT (Luby Transform) codes, which are a special class of Fountain codes, are widely used in Wireless Sensor Networks (WSNs) to increase the robustness of data storage and efficiency of data retrieval. In this paper, we propose a novel LT codes based Distributed Coding (LTDC) scheme for efficient distributed storage in WSNs. In the proposed LTDC scheme, we use random walks to disseminate sensed data from a source sensor node to a random subset of sensor nodes by multicast. As long as a data packet stops at an ending sensor node of a random walk, the ending sensor node encodes this data packet in a main packet (an encoded data packet) with a certain probability. By adjusting the main packet with the un-encoded data packets, the number of data packets encoded in the main packet follows the distribution of LT codes. The data collector is able to decode the original data by querying any subset of sensor nodes. The theoretical analysis and simulation results have demonstrated that the proposed LTDC scheme has lower data dissemination cost and lower storage overhead, while maintains the same level of fault tolerance as the original LT codes.

Due to the dynamically changing topology of Internet of Vehicles (IoV), it is a challenging issue to achieve efficient data dissemination in IoV. This paper considers strongly connected IoV with a number of heterogenous vehicular nodes to disseminate information and studies distributed replication-based data dissemination algorithms to improve the performance of data dissemination. Accordingly, two data replication algorithms, a deterministic algorithm and a distributed randomised algorithm, are proposed. In the proposed algorithms, the number of message copies spread in the network is limited and the network will be balanced after a series of average operations among the nodes. The number of communication stages needed for network balance shows the complexity of network convergence as well as network convergence speed. It is proved that the network can achieve a balanced status after a finite number of communication stages. Meanwhile, the upper and lower bounds of the time complexity are derived when the distributed randomised algorithm is applied. Detailed mathematical results show that the network can be balanced quickly in complete graph; thus highly efficient data dissemination can be guaranteed in dense IoV. Simulation results present that the proposed randomised algorithm outperforms the present schemes in terms of transmissions and dissemination delay.

Wireless sensor networks require communication protocols for efficiently propagating and maintaining data in a distributed fashion. Ideally, a communication protocol is able to disseminate data quickly to all nodes in the network using as few transmissions as possible, while distributing transmission load fairly. In the context of wireless sensor networks the Trickle algorithm is a popular protocol serving as the basis for many of the current communication protocols. In this study we analyze the performance of Trickle with respect to efficiency, fairness, reliability and propagation speed. Additionally, we show how small extensions to the algorithm can improve its performance.

How to get a better balance between the push and pull of the event data is the common goal of the various data dissemination algorithms in wireless sensor networks (WSNs). At this basis, the load of the system hotspot should be further decreased and the number of the event data replicas should be reduced. In the paper, two well-known typical structured and unstructured data dissemination algorithms (DCS and CN) are analyzed and their advantages and disadvantages are figured out. At the same time, by combining the push- pull strategies of the two algorithms, two new data dissemination algorithms are proposed based on the hybrid structured and unstructured data push-pull strategies for the ALL-type queries in the different application situation in WSNs. The two new algorithms are hybrid-Dcs-Cn1 (HDC1) and hybrid-Dcs-Cn2 (HDC2). The theoretical analyses and simulations show that they resolve the problems of the high load of the hotspot and the large number of the event data replicas and the low performance of the query on the premise that ensuring the balance between the push and pull. The total system communication cost of the two new algorithms are close to or better than the existing algorithms, so the two algorithms are more appropriate for the WSNs , especially when there are a large volume of data to be dealt with, and they are two energy-efficient data dissemination algorithms.

In cloud era as the data stored is enormous, efficient retrieval of data with reduced latency plays a major role. In cloud, owing to the size of the stored data and lack of locality information among the stored files, metadata is a suitable method of keeping track of the storage. This paper describes a novel framework for efficient retrieval of data from the cloud data servers using metadata with less amount of time. Performance of queries due to availability of files for query processing can be greatly improved by the efficient use of metadata and its analysis thereof. Hence this paper proposes a generic approach of using metadata in cloud, named ‘MaaS—Metadata as a Service.’ The proposed approach has exploited various methodologies in reducing the latency during data retrieval. This paper investigates the issues on creation of metadata, metadata management and analysis of metadata in a cloud environment for fast retrieval of data. Cloud bloom filter, a probabilistic data structure used for efficient retrieval of metadata is stored across various metadata servers dispersed geographically. We have implemented the model in a cloud environment, and the experimental results show that methodology used is efficient in increasing the throughput and also by handling large number of queries efficiently with reduced latency. The efficacy of the approach is tested through experimental studies using KDD Cup 2003 dataset. In the experimental results, proposed ‘MaaS’ has outperformed other existing methods.