Dong Kun Noh

Attribute-Based Access Control with Efficient Revocation in Data Outsourcing Systems

Some of the most challenging issues in data outsourcing scenario are the enforcement of authorization policies and the support of policy updates. Ciphertext-policy attribute-based encryption is a promising cryptographic solution to these issues for enforcing access control policies defined by a data owner on outsourced data. However, the problem of applying the attribute-based encryption in an outsourced architecture introduces several challenges with regard to the attribute and user revocation. In this paper, we propose an access control mechanism using ciphertext-policy attribute-based encryption to enforce access control policies with efficient attribute and user revocation capability. The fine-grained access control can be achieved by dual encryption mechanism which takes advantage of the attribute-based encryption and selective group key distribution in each attribute group. We demonstrate how to apply the proposed mechanism to securely manage the outsourced data. The analysis results indicate that the proposed scheme is efficient and secure in the data outsourcing systems.

Using a dynamic backbone for efficient data delivery in solar-powered WSNs

The periodic nature of solar power requires a different approach to energy consumption in wireless sensor networks (WSNs) from battery-based WSNs. Based on the energy model of a solar-powered node, we develop efficient energy-aware topology-control and routing schemes which utilize a backbone network consisting of energy-rich nodes within the WSN. This backbone handles most of the traffic with low latency, while reconfiguring itself dynamically in response to changes in the availability of energy at each node. Simulation results demonstrate that our schemes can achieve a balance between latency and energy consumption.

Reliable Wildfire Monitoring with Sparsely Deployed Wireless Sensor Networks

This paper proposes a reliable wildfire monitoring system based on a wireless sensor network (WSN) sparsely deployed in adverse conditions. The physical environment under consideration is characterized by asymmetric, irregular, and unreliable wireless links, inadequate Fresnel zone clearance, and routing problems, to name a few. We use reliable communication schemes on a fault-tolerant network topology, where sensory data are guaranteed to reach the base station with organized data storage and real-time visualization. Our approach has been validated experimentally for the case of peat-forest wildfire in southern Borneo where the fire breaks out frequently.

SolarCastalia: solar energy harvesting wireless sensor network simulator

Most existing simulators for WSNs (wireless sensor networks) model battery-powered sensors and provide MAC and routing protocols designed for battery-powered WSNs. Recently, however, increasingly extensive studies of energy harvesting sensor systems require the development of appropriate simulators, but there are few related studies on such simulators. Unlike existing simulators, simulators for energy harvesting WSNs require a new energy model that is integrated with the energy harvesting, rechargeable battery, and energy consuming models. Additionally, the new model must enable applications of the well-known MAC and routing protocols designed for energy harvesting WSNs and have a convenient user-friendly interface. In this work, we design and implement a user-friendly simulator for solar energy harvesting WSNs.

Adaptive Data Aggregation and Compression to Improve Energy Utilization in Solar-Powered Wireless Sensor Networks

A node in a solar-powered wireless sensor network (WSN) collects energy when the sun shines and stores it in a battery or capacitor for use when no solar power is available, in particular at night. In our scheme, each tiny node in a WSN periodically determines its energy budget, which takes into account its residual energy, and its likely acquisition and consumption. If it expects to acquire more energy than it can store, the data which has it has sensed is aggregated with data from other nodes, compressed, and transmitted. Otherwise, the node continues to sense data, but turns off its wireless communication to reduce energy consumption. We compared several schemes by simulation. Our scheme reduced the number of nodes forced to black out due to lack of energy so that more data arrives at the sink node.

Energy-aware data aggregation scheme for energy-harvesting wireless sensor networks

In energy-harvesting wireless sensor networks (WSNs), not only energy savings but also efficient energy utilization is required. This study suggests a scheme that indicates when to send data decided by predicting the remaining energy of a node, and aggregates sensed data to increase the amount of data arrived at the sink node. With this method, if the estimated remaining energy of a node is expected to run over the capacity, it transmits aggregated data or else it turns off its radio and only stores sensed data to decrease the blackout time of nodes. Simulation results show that the proposed scheme decreases the blackout time of nodes and increases the data collecting rate efficiently compared to both normal data sending and the specific amount of aggregated data sending cases.

Energy-Aware hierarchical topology control for wireless sensor networks with energy-harvesting nodes

The nodes in a wireless sensor network (WSN) are often clustered to improve the efficiency of data transmission, but this can cause an imbalance in the energy available at each node. This impairs the transmission capability of some nodes and hence reduces network connectivity. This imbalance worsens as nodes become defunct. We propose a multilayer topology for long-term hybrid WSNs which contain both battery-powered and energy-harvesting nodes. Each node periodically selects its own layer, depending on the energy it has available, in order to balance energy levels and maintain network connectivity. Simulations show that this scheme improves the delivery of data across a WSN.

Multi-layer topology control for long-term wireless sensor networks

Due to the inefficiency of a flat topology, most wireless sensor networks (WSNs) have a cluster or tree structure; but this causes an imbalance of residual energy between nodes, which gets worse over time as nodes become defunct and replacements are inserted. Multiple layers are better then the typical two-layer cluster-based topology, because it can better accommodate nodes with different levels of residual energy. We propose that each node should periodically determine its own layer, as its situation and the network topology changes. We introduce a topology control scheme for long-term WSNs with these features. Simulations show that this scheme can balance node energy levels, and thus extend network lifetime.

Energy-aware determination of compression for low latency in solar-powered wireless sensor networks

There have been many studies performed about increasing network lifetime in wireless sensor networks that involve reducing data size, since the data transmission process takes up a large part of energy consumption. However, reducing data size results in increased delay time due to not only the compression computation time but also the waiting time to gather a sufficient amount of data for compression. Meanwhile, in solar-powered wireless sensor networks, the harvested energy may be surplus to the basic operations of sensor nodes. In this study, such surplus energy is utilized to reduce the delay time between nodes. Nodes with residual energy less than a certain threshold transfer data with compression in order to reduce energy consumption, and nodes with residual energy over the threshold (which means there is surplus energy) transfer data without compression to reduce the delay time between nodes by using the surplus energy. Simulation-based performance verifications show that the technique proposed in this study exhibits optimal performance in terms of both energy and delay times compared with traditional methods.

Adaptive sensing and compression rate selection scheme for energy-harvesting wireless sensor networks

In wireless sensor networks, a node that increases its sensing rate to gather more data consumes more energy. Consequently, it will be exhausted earlier than other nodes. In this article, we propose a sensing rate control scheme to increase data resolution without increasing the number of blackout nodes in energy-harvesting wireless sensor networks. In the proposed scheme, each node selects a compression algorithm and adjusts the sensing rate to gather more data while the node operates properly. When a node is estimated to have extra energy, it gathers additional data. In contrast, when it is estimated to be exhausted, it gathers less data to save energy. It also selects an appropriate compression algorithm according to the compression ratio of the selected algorithm to reduce the workload of intermediate nodes. An experimental result verifies that the proposed scheme gathers more data with a lower number of blackout nodes than other schemes.