Jongjun Park

DualMOP-RPL: Supporting Multiple Modes of Downward Routing in a Single RPL Network

RPL is an IPv6 routing protocol for low-power and lossy networks (LLNs) designed to meet the requirements of a wide range of LLN applications including smart grid AMIs, home and building automation, industrial and environmental monitoring, health care, wireless sensor networks, and the Internet of Things (IoT) in general with thousands and millions of nodes interconnected through multihop mesh networks. RPL constructs tree-like routing topology rooted at an LLN border router (LBR) and supports bidirectional IPv6 communication to and from the mesh devices by providing both upward and downward routing over the routing tree. In this article, we focus on the interoperability of downward routing and supporting its two modes of operations (MOPs) defined in the RPL standard (RFC 6550). Specifically, we show that there exists a serious connectivity problem in RPL protocol when two MOPs are mixed within a single network, even for standard-compliant implementations, which may result in network partitions. To address this problem, this article proposes DualMOP-RPL, an enhanced version of RPL, which supports nodes with different MOPs for downward routing to communicate gracefully in a single RPL network while preserving the high bidirectional data delivery performance. DualMOP-RPL allows multiple overlapping RPL networks in the same geographical regions to cooperate as a single densely connected network even if those networks are using different MOPs. This will not only improve the link qualities and routing performances of the networks but also allow for network migrations and alternate routing in the case of LBR failures. We evaluate DualMOP-RPL through extensive simulations and testbed experiments and show that our proposal eliminates all the problems we have identified.

Node distribution-based localization for large-scale wireless sensor networks

Distributed localization algorithms are required for large-scale wireless sensor network applications. In this paper, we introduce an efficient algorithm, termed node distribution-based localization (NDBL), which emphasizes simple refinement and low system-load for low-cost and low-rate wireless sensors. Each node adaptively chooses neighboring nodes, updates its position estimate by minimizing a local cost-function, and then passes this updated position to neighboring nodes. This update process uses a node distribution that has the same density per unit area as large-scale networks. Neighbor nodes are selected from the range in which the strength of received signals is greater than an experimentally based threshold. Based on results of a MATLAB simulation, the proposed algorithm was more accurate than trilateration and less complex than multi-dimensional scaling. Numerically, the mean distance error of the NDBL algorithm is 1.08–5.51 less than that of distributed weighted multi-dimensional scaling (dwMDS). Implementation of the algorithm using MicaZ with TinyOS-2.x confirmed the practicality of the proposed algorithm.

ReLiSCE: Utilizing Resource-Limited Sensors for Office Activity Context Extraction

The capability to extract human activity context in a room environment can be used as meaningful feedback for various wireless indoor application systems. Being able to do so with easily installable resource-limited sensing components can even further increase the systems applicability for various purposes. This paper introduces our efforts to design a system consisting of heterogeneous low-cost, resource-limited, wireless sensing platforms for accurately extracting the human activity context from an indoor environment. Specifically, we introduce Resource Limited Sensor-based activity Context Extraction (ReLiSCE), a system consisting of microphone array, passive infra-red (PIR), and illumination sensors that effectively detect the activities that occur in an office (meeting room) environment. The signal processing schemes used in ReLiSCE are designed so that their size and complexity is suitable for the resource limitations that many embedded computing platforms introduce. Using empirical evaluations with a prototype system, we show that despite the simplicity of its data processing schemes, ReLiSCE successfully classifies human activity states in various meeting scenarios. Furthermore, we show that high accuracy is achieved by combining results from heterogeneous sensors. We foresee this paper as a sub-system that interconnects with various application systems for autonomously configuring peoples everyday living environments in a more comfortable and energy-efficient manner.

An efficient data fusion and assurance mechanism using temporal and spatial correlations for home automation networks

In this paper, we propose an efficient and accurate data fusion mechanism through temporal and spatial correlations for Home Automation Networks. Data fusion is a process to decrease the transmission number of similar sensory values, thus its importance is in the spotlight because energy efficiency is a main issue on the networks. In order to reduce the transmission amount of data, each device decides whether to send new sensing data or not after comparing it with previous sensing values(temporal correlation) and the last transmitted value of the neighbor device(spatial correlation) which is a one-hop range via a predetermined user threshold. Our mechanism includes a commit and assurance process for security enhancement. For the performance evaluation of our mechanism, we use the temperature data measured on real networks and extend them using Gaussian distribution in order to obtain the more test data sets. The simulation results show that using both types of correlations is more efficient than just using one type for data fusion in terms of the data transmission amount and accuracy. Moreover, our simulation for the commit and assurance process demonstrates only slightly more energy.

Low-power and topology-free data transfer protocol with synchronous packet transmissions

Tightly synchronizing transmissions of the same packet from different sources theoretically results in constructive interference. Exploiting this property potentially speeds up network-wide packet propagation with minimal latencies. Our empirical results suggest the timing constraints can be relaxed in the real world, especially for radios using lower frequencies such as the IEEE 802.15.4 radios at 900 MHz. Based on these observations we propose PEASST, a topology-free protocol that leverages synchronized transmissions to lower the cost of end-to-end data transfers, and enables multiple traffic flows. In addition, PEASST integrates a receiver-initiated duty-cycling mechanism to further reduce node energy consumption. Results from both our Matlab-based simulations and indoor testbed reveal that PEASST can achieve a packet delivery latency matching the current state-of-the-art schemes that also leverages synchronized transmissions. In addition, PEASST reduces the radio duty-cycling by three-fold. Furthermore, comparisons with a multi-hop routing protocol shows that PEASST effectively reduces the per-packet control overhead. This translates to a ~10% higher packet delivery performance with a duty cycle of less than half.

Improving the Packet Delivery Performance for Concurrent Packet Transmissions in WSNs

In this letter, we investigate the properties of packet collisions in IEEE 802.15.4-based wireless sensor networks when packets with the same content are transmitted concurrently. While the nature of wireless transmissions allows the reception of a packet when the same packet is transmitted at different radios with (near) perfect time synchronization, we find that in practical systems, platform specific characteristics, such as the independence and error of the crystal oscillators, cause packets to collide disruptively when the two signals have similar transmission powers (i.e., differences of <;2 dBm). In such scenarios, the packet reception ratio (PRR) of concurrently transmitted packets falls below 10%. Nevertheless, we empirically show that the packet corruption patterns are easily recoverable using forward error correction schemes and validate this using implementations of RS and convolutional codes. Overall, our results show that using such error correction schemes can increase the PRR by more than four-fold.

Multitiered and Distributed WSAN for Cooperative Indoors Environment Management

For the past decade, wireless sensor networks have focused primarily on data collection. As a result the network topology for these systems was usually heavily centralized. However, for these networks to form a full system, the introduction of proper actuation units and decision-making intelligence is inevitable. Such a new wireless sensor and actuator network system enables new architectural research issues that have not been previously studied. In this work, we introduce the DWSAN system architecture, which effectively combines both sensor and actuation hardware devices to a single network and manages this network so that the actuation decisions are made in a distributed manner and the topology of the network maintains a multitier architecture. Our intensive set of evaluations reveal that, compared to the centralized approach that has been used in most wireless sensor network systems until now, when actuation units are introduced to the system, the DWSAN architecture reduces the transmission load of the network and the actuation decision-making latency by close to twofold and threefold, respectively. Furthermore, we show that this benefit naturally leads to better scalability of the system, making it suitable for various sensing applications in different environments.

Towards full RPL interoperability: addressing the case with downwards routing interoperability

In this work we point out the issue of the IETF RPL routing protocols two different downwards routing schemes not being able to interoperate with each other. This problem is less of an issue when low-power and lossy networks (LLNs) are deployed homogeneously but with the industrial kickoff and large scale deployments, the interoperability of heterogeneous, standards-compliant implementations will become a significant issue. To address this, we suggest two major changes to IETF RPL (RFC 6550). First we suggest that all storing mode nodes should hold the capability to understand and attach source routing headers that the non-storing mode nodes require to forward packets. Next, we suggest that RPLs non-storing mode nodes should send their destination advertisement messages hop-by-hop, rather than the current end-to-end approach. We show, with two different IPv6 implementations in TinyOS and NanoQplus, that our suggestions high achieve high interoperability performance among different implementations for downwards traffic patterns.

Just send me the summary!: analyzing sensor data for accurate summary reports in indoor environments

As the number of sensors increase in wireless sensing applications, it is important for nodes to provide meaningful summary reports of the original data to the gateway. In doing so, given the resource constraints of the sensing devices, we need a light weight, yet, effective scheme to minimize the number of reports at the sensors while preserving the accuracy of the original data. However, we show in this work that unlike outdoors environments where various sensors may show a similar phenomena (e.g., high spatial correlation), this may not be true for sensors deployed in a typical indoors environment. To resolve this issue, we introduce a data summarizing scheme for such indoor applications that combines two techniques. First, our scheme detects events in a data stream by comparing the short term mean of the recent measurements with the most recent report sent to the gateway. Second, we include an exponentially increasing/decreasing timer that triggers additional reports where the timers interval is reconfigured dynamically with respect to the result of our event detection method. Evaluations with temperature and humidity data collected in an indoors environment indicate that our scheme significantly reduces the number of transmissions while maintaining a mean error as low as ~0.07°C and ~0.08%RH.

Projecting household-scale utility usage: a case study using a long-term dataset

The deployment of advanced metering infrastructures allows suppliers and consumers to better understand the utility supply and usage chain. Data from these systems are typically used to analyse utility usage in a large scale, but when observed at smaller scales, we can enable a number of interesting new application. In this work we use utility usage data collected from 300 households over three years and perform detailed analysis to understand per-household utility usage patterns. We show that per-household utility usage data introduces high variances and low correlations among different households even if they are co-located in similar geographical regions. Using our findings, we introduce AUUP, an adaptive utility usage prediction scheme that combines the output from different existing forecasting schemes to adaptively make smart small-scale utility usage predictions. Our evaluations show that AUUP effectively reduces the prediction errors of artificial neural networks, LMS and Kalman filter-based AR model prediction schemes.