The increasing use of electric vehicles (EVs) has highlighted the need for sustainable recycling and traceability of essential raw materials. This study introduces a blockchain and AI-integrated framework designed with the possibility to optimize vehicle components lifecycle management with recycling and tracing throughout the supply chain. The system uses Decentralized Identifiers (DIDs) for secure identification of components, enabling transparent tracking from production to recycling. Hyperledger Fabric ensures immutable data an choring across stakeholders. Hyperledger Caliper is used for benchmarking the system, assessing metrics such as transaction speed, latency, and scalability. AI models, including regression and clustering algorithms, are utilized to optimize recycling processes, predict component lifespans, and enhance resource recovery in the system. A tokenized reward mechanism incentivizes eco-friendly practices among stakeholders. The system also shows its environmental benefits, including energy savings from improved recycling performance. The proposed framework effectively supports the circular economy by enhancing resource recovery processes. While its design has the potential to reduce environmental impact, this benefit depends on the reusable, modular system design.

The rapid evolution of digital identity verification demands solutions that balance security, privacy, and efficiency. The electronic know your customer (eKYC) is a technological integration for client identification. It automates the process, reducing costs related to traditional know your customer (KYC). This includes eliminating paper-based document management, reducing manpower needs, and minimizing human errors. This systematic literature review (SLR) uses the preferred reporting items for systematic reviews and meta-analyses (PRISMA) model to investigate the revolutionary potential of blockchain-based electronic KYC (eKYC), focusing on self-sovereign identity (SSI) and Decentralized Identifiers (DID). The evaluation summarizes the current state by critically assessing 44 selected research works from an initial pool of 367. Our findings show that decentralized eKYC improves security with tamper-proof credentials and cryptographic verification. SSI and DID give users control over their data and selective disclosure. However, there are key limitations: 1) a focus on financial applications, ignoring Internet of Things (IoT) integration; 2) a lack of comprehensive technical analysis on scalability and interoperability; and 3) limited real-world case studies on regulatory compliance and challenges. This work combines insights from research and industry, highlighting the need for regulatory collaboration, hybrid architectures for scalability, and user-centric design. In addition, most identity management solutions are based on Ethereum (33%), followed by Hyperledger (18%). Around 51% of solutions use smart contracts, with banking (23%) and the financial industries (19%) being the primary adopters. It emphasizes the importance of standardized eKYC protocols, technical evaluations, and interdisciplinary collaboration for practical adoption across sectors.

Proposals for Ultra-Large-Scale-System (ULSS), particularly the grid’s energy management systems (EMSs), to adopt the Ethereum blockchain are increasing as its support for privacy-preserving, encrypted, decentralized computing via sharding, rollups, Smart-Contracts (SC), and Zero-Knowledge-proofs (ZK) address the increasing topological, behavioral, and data-processing challenges. In this context, the aggregation and verification of aggregated, ZK Kate-Zaverucha-Goldberg constant-sized polynomials commitments (KZG) are a bottleneck limiting deployment to Internet-of-Things (IoT) nodes used by the EMS due to high O(b G+blog2b F) computationincurred when aggregating or verifying by recreation. The alternative, expensive pairing checks involve two pairings, three exponentiations (Exp), three multiplications (Mul), and one addition (Add), a security factor S times for the n aggregated KZG. The proposed pairing checks significantly reduce costs for both: 1) Verifiers: two pairings, no Exp, one Mul, and one Add, and 2) Provers: one pairing check, no Exp, four Mul, and one Add. The aggregation method, based on multidimensional differential addition chains, costs only O(ℓ) computation, where ℓ is the bit length of the scalars. This approach demonstrates the feasibility of operating a KZG-centric blockchain with KZG rollups on IoT networks, marking a significant advancement in ULSS.

Process mining discovers and analyzes a process model from historical event logs. The prior art methods use the key attributes of case-id, activity, and timestamp hidden in an event log as clues to discover a process model. However, a user needs to specify them manually, and this can be an exhaustive task. In this article, we propose a two-stage key attribute identification method to avoid such a manual investigation, and thus this is a step toward fully automated process discovery. One of the challenging tasks is how to avoid exhaustive computation due to combinatorial explosion. For this, we narrow down candidates for each key attribute by using supervised machine learning in the first stage and identify the best combination of the key attributes by discovering process models and evaluating them in the second stage. Our computational complexity can be reduced from$\mathcal {O}(N^{3})$to$\mathcal {O}(k^{3})$where$N$and$k$are the numbers of columns and candidates we keep in the first stage, respectively, and usually$k$is much smaller than$N$. We evaluated our method with 14 open datasets and showed that our method could identify the key attributes even with$k = 2$for about 20 seconds for many datasets.

The advent of federated learning (FL) has sparked a new paradigm of parallel and confidential decentralized machine learning (ML) with the potential of utilizing the computational power of a vast number of Internet of Things (IoT), mobile, and edge devices without data leaving the respective device, thus ensuring privacy by design. Yet, simple FL frameworks (FLFs) naively assume an honest central server and altruistic client participation. In order to scale this new paradigm beyond small groups of already entrusted entities toward mass adoption, FLFs must be: 1) truly decentralized and 2) incentivized to participants. This systematic literature review is the first to analyze FLFs that holistically apply both, the blockchain technology to decentralize the process and reward mechanisms to incentivize participation. 422 publications were retrieved by querying 12 major scientific databases. After a systematic filtering process, 40 articles remained for an in-depth examination following our five research questions. To ensure the correctness of our findings, we verified the examination results with the respective authors. Although having the potential to direct the future of distributed and secure artificial intelligence, none of the analyzed FLFs is production ready. The approaches vary heavily in terms of use cases, system design, solved issues, and thoroughness. We provide a systematic approach to classify and quantify differences between FLFs, expose limitations of current works and derive future directions for research in this novel domain.

Identifying objects with radar is in great demand to avoid road accidents. Recent research tried to identify moving objects on a road by inputting radar information to a machine learning classifier. In the conventional method, the features used in the machine learning are extracted from the observed radar information with a short time interval. Since the movement of the objects is different depending on the objects, time series information is effective for classification, which has not been exploited before. In this paper, we propose an on-road object identification considering time series of radar information. We measured objects with 79.5 GHz millimeter-wave radar and extract features from a series of time windows by calculating the mean and variance of object information, i.e., velocity, distance, and signal power. The classification performance was evaluated with a dataset obtained by on-road experiments. It is shown that our method outperforms the conventional one and the proposed features significantly contribute to the accurate identification.

CIA (Confidentiality, Integrity and Availability) is getting more and more important in the cloud based manufacturing and IIoT (Industrial Internet-of-Things)/Indutry 4.0 domain. Recently, blockchain, a decentralized ledger technique, has attracted considerable attention to realize secure and robust data management systems for IIoT. Although several blockchain-based data management platforms for IIoT have been proposed, sensor data are stored outside of the blockchain, meaning that the data integrity and accessibility are not improved. In this paper, we take a different blockchain-based approach for sensor data management; any important sensor data are stored in the blockchain. For realizing this, a series of sensor data is compressed-then-stored in the blockchain by leveraging a fact that many sensor data often have a certain level of periodicity and stability. We have tested our idea against the real sensor data measured at our model factory, and evaluated several performance metrics such as compression ratio, compression/decompression time, process time required to store and retrieve sensor data from our Proof-of-Concept system with Ethereum and the expected blockchain size. From the results, it can be concluded that the proposed platform is viable for small factory cases but needs more technological advancement is required for large scale cases.

Recent technological evolution enables Artificial Intelligence (AI) model training by users' mobile devices, which accelerates decentralized big data analysis. In particular, Federated Learning (FL) is a key enabler to realize decentralized AI model update without user's privacy disclosure. However, since the behaviour of workers, who are assigned a training task, cannot be monitored, the state-of-the-art methods require a special hardware and/or cryptography to force the workers behave honestly, which hinders the realization. Furthermore, although blockchain-enabled FL has been proposed to give workers reward, any rigorous reward policy design has not been discussed. In this paper, to tackle these issues, we present a novel method using mechanism design, which is an economic approach to realize desired objectives under the situation that participants act rationally. The key idea is to introduce repeated competition for FL so that any rational worker follows the protocol and maximize their profits. With mechanism design, we propose a generic full-fledged protocol design for FL on a public blockchain. We also theoretically clarify incentive compatibility based on contest theory which is an auction-based game theory in economics.

With the increase in the population of elderly, a technique for monitoring them is receiving more and more attention all over the world. In particular, it is highly demanded to develop the system that detects a fainting elderly in a toilet, since such an accident is likely to get worse due to the late detection. In this paper, we propose an anomaly detection method in toilet using an FMCW(Frequency Modulated Continuous Wave) radar, which can measure the distance between an object and an FMCW radar. As anomalies, we determine as the situations where a subject faints while stooping on the toilet seat, leaning on the backrest, leaning on the side wall, and lying on the floor after the fall. With an FMCW radar, a fainting subject could be detected based on whether the estimated distance between a subject and an FMCW radar is almost constant. However, a subject might be sitting still, and also the distance to not a subject but a floor could be estimated depending on a subject's posture. To address this problem, in the proposed method, the time when the estimated distance is almost constant is detected, and then the detected time is classified into the time due to a faint or a non-faint by a supervised machine learning classifier. The used features are extracted based on the data, e.g., the estimated distance, just before the detected time. To evaluate the anomaly detection accuracy of the proposed method, we conducted the experiments to observe not only the aforementioned abnormal behavior but also the normal behavior, e.g., the button operation and sitting down. As a result, in terms of the classification of abnormal and normal behavior, our proposed method achieved the F- measure of 0.91, where F-measure is the overall score of recall and precision rates.

Respiration is known to reflect our health condition, which motivates researchers to develop various radar-based respiration rate estimation methods. However, these conventional methods do not work, when a subject is not right in front of the radar. In this paper, we propose a novel CNN (Convolutional Neural Network)-based respiration rate estimation method in indoor environments via a MIMO (Multiple-Input Multiple-Output) FMCW (Frequency Modulated Continuous Wave) radar. A MIMO FMCW radar can estimate the DoA (Direction of Arrival) and the distance between a MIMO FMCW radar and an object. Thus, the respiration can be captured based on the phase variation at a subject's location. However, even when the advanced signal processing, e.g., MUSIC (MUltiple SIgnal Classification) algorithm, is used, it is difficult to estimate the DoA and the distance in indoor environments due to the large effect of multipath. To deal with this problem, in the proposed method, phase variations against various locations are calculated from the received signals of a MIMO FMCW radar, and then spectrograms are calculated from the phase variations. Each spectrogram is subsequently fed into the CNN that outputs the respiration rates, e.g., 0.1 Hz, 0.2 Hz, and non-respiration, i.e., a spectrogram without the effect of respiration, where is one of the deep learning techniques that have been successfully applied to the image recognition. Through the experiments we confirmed that except for when microwaves were not transmitted directly toward a subject's chest due to furniture, the proposed method accurately estimated the respiration rate, regardless of the situation.

Heartbeats and a blink are two major physiological signals that provide crucial information on our stress and drowsiness. Hence, it is highly demanded to detect simultaneously heartbeats and blinks in many applications. A Doppler sensor could be a key device to facilitate the non-contact heartbeat and blink detection in daily life. Although many Doppler sensor-based heartbeat and blink detection methods have been independently proposed, when heartbeats and blinks are detected simultaneously with one Doppler sensor, the detection accuracies of such heartbeat and blink detection methods get degraded because of at least two issues: (i) the low SNR (Signal-to-Noise Ratio) of each signal reflected from a subject's chest and face, and (ii) the similarity of the spectrum distribution of heartbeats and a blink. In this paper, we propose a spectrogram-based simultaneous heartbeat and blink detection using one Doppler sensor. In the proposed method, to extract the spectra that might be due to heartbeats and a blink, the spectra on a spectrogram are integrated. Blink detection is then performed by classifying the peaks of the integrated spectrum into a peak due to a blink or a non-blink based on a supervised machine learning classifier trained with a set of the time domain and the time-frequency domain features. Based on the non-blink peaks, heartbeats are detected considering the RRI (R-R Interval) estimated before the investigated peak to prevent the incorrect heartbeat detection. Through the experiments in the case where microwaves are transmitted from one Doppler sensor toward a body including a chest and a face, our proposal has been shown to be able to simultaneously detect heartbeats and blinks with high accuracy.

Recently, through exploiting the spectral sparsity of heartbeat component, a heartbeat detection method using a stochastic gradient approach has enabled a high-resolution of heartbeat spectrum reconstruction by Doppler radar signal, which also suppresses the residual noises after signal decomposition. However, the interference from respiration and/or body motion often corrupts the decomposition of signal by singular spectrum analysis (SSA), resulting in an inaccurate extraction of heartbeat component. In this paper, a non-negative matrix factorization (NMF)-based blind source separation (BSS) is first applied to non-contact heartbeat detection for better heartbeat extraction, incorporating the stochastic gradient approach. Specifically, motion noise is taken into account as one of sources, achieving relatively stable separation in various scenarios. In our proposed BSS approach, the spectrogram originated from radar signal is decomposed twice by NMF, which is used to learn the basis spectra (BS) relying on spectral correlation. Experimental results showed the improved accuracy and robustness of our method over conventional methods, on the heart rate (HR) measurement against subjects' sitting still or typewriting.

Injuries caused by falls are one of the major threats for the elderly. Thus, the demand for a fall detection system without requiring a user to wear or equip any devices is rapidly increasing. In this paper, a fall detection system using Ultra-High Frequency (UHF) passive Radio-Frequency IDentification (RFID) is presented. With the RFID reader on the ceiling and multiple RFID tags on the floor, anomaly scores are computed based on the neighborhood preservation principle. This focuses only on the correlation between RSSI fluctuations between a pair of tags. Therefore, unlike the conventional methods, our method manages to detect falls without requiring a large number of reference data. To detect falls in several areas of the room, our system only requires a simple reference data with one subject walking around the room. Experiments are conducted in an indoor environment with two different setups to show the effectiveness of our method. Our method improves the conventional one, and achieves a high Area Under Curve (AUC) of 0.98.

Heartbeat detection are receiving a lot of attention in the field of health care, since cardiac activity reflects various information of a subject, e.g., the stress. Many Doppler sensor-based heartbeat detection methods have been proposed so far. As one of such methods, the MUSIC (MUltiple SIgnal Classification)-based HR (Heart Rate) estimation method has been proposed. However, the conventional MUSIC-based HR estimation method not only needs a long time window, but also requires to know the number of sinusoidal signals composing the analyzed signals, P, in advance of applying MUSIC to the analyzed signal, which is challenge. In this paper, we propose a MUSIC-based HR estimation method with the DCT (Discrete Cosine Transform)-based parameter P selection. In the proposed method, the analyzed signal is firstly decomposed by DCT. The inverse DCT is then performed based on only components that might be related with heartbeats. The number of components used in the inverse DCT is selected as P. Through the experiments, we confirmed that our method outperformed the conventional one by the estimation accuracy of the HR and the stress indexes such as CVI (Cardiac Vagal Index) and CSI (Cardiac Sympathetic Index).

The number of elderly people who live alone is increasing in many countries. Furthermore, many of their accidents occur at home. Hence, it is an urgent demand for a system monitoring their activities to detect accidents indoor such as falling. In conventional systems of fall detection using array antennas, falling after standing still can be detected with high accuracy by leveraging the features indicating the change of radio wave propagation. However, it is difficult to detect falling after walking correctly. In this paper, to improve fall detection accuracy including falling after walking, we propose an accurate fall detection system by leveraging the features indicating the change of Doppler signals during human activities in detail. Analyzing Doppler signals is useful to detect falling since they are observed when a radio wave reflects by moving objects. We conducted experiments in actual rooms to demonstrate that the proposed method can detect falling after both standing and walking with high accuracy.

It is known that the blink duration is highly related to drowsiness, where the blink duration is the entire duration of one blink. Hence, it is important to measure the blink duration without any special wearable devices in various application, e.g., driver's monitoring. Although a Doppler sensor could be a key device to realize it, no such blink duration estimation method has been realized so far, since estimating the blink duration is difficult because of the low SNR (Signal to Noise Ratio) of the signal reflected from eyelids. In this paper, we propose a Doppler sensor-based method that estimates the duration, tblink, that is proportional to the actual blink duration. In the proposed method, a spectrogram is firstly calculated from a received signal, and then the timings when eyelids close and open are extracted from that. More specifically, tblink is calculated based on the center-of-gravity of the energy on a spectrogram. We conducted the experiments on five subjects to show the estimation accuracy of our proposed method. We confirmed that our method achieved the average correlation coefficient R of 0.95, furthermore, the average RMSE (Root Mean Square Error) of 46 ms.

Heart-rate variability (HRV) is closely related with physical or mental conditions. It is rather necessary to develop remote monitoring technologies to reduce subjects' pressure, e.g., heart-rate (HR) monitoring to drivers. Recently, the contactless heartbeat detection with Doppler radar has drawn extensive attention, due to less burden on subjects. However, the received signals of Doppler radar are easily contaminated by respiration or body motion, resulting in performance degradation. In this paper, to realize robust heartbeat detection with Doppler radar, a stochastic gradient approach is first proposed to reconstruct heartbeat spectrum. By correcting the gradient of cost function constructed by recursion error, and utilizing the sparse characteristics of heartbeat spectrum, the reconstructed spectrum can be obtained with minimum deviation. Furthermore, the zero-attracting sign least mean square (ZA- SLMS) algorithm based on stochastic gradient descent (SGD) is proposed, to accomplish more stable sparse spectrum reconstruction (SSR), by quantizing the updating of recursion error. Finally, HR is estimated by spectral peak tracking consisting of peak selection and verification. Experimental results validate that the proposed method can significantly improve detection accuracy over the conventional methods, based on the measurements from 5 subjects during sitting still or typing with a laptop.

In order to fix a bug or install a new application, disseminating a code to specific target sensors is an important management task in wireless sensor networks. In order to disseminate a code to specific target sensors in an energy efficient manner, it is required to select the minimum required number of forwarders with a small number of control messages. In this paper, we propose an energy efficient target specific code dissemination scheme with a forwarder selection algorithm. The proposed scheme consists of two steps, (i) a forwarder selection algorithm with a control message suppression, (ii) unnecessary forwarders reduction with a sleep mechanism. In order to select the minimum required forwarders with a small number of control messages, we propose an algorithm to select a forwarder where more target sensors are reachable, through a single control message by leveraging overhearing of control messages. In addition, in order to reduce the number of forwarders, we propose a sleep mechanism to put unnecessary forwarders to sleep where any target sensor is not reachable. A performance analysis has shown that the number of control messages of the proposed scheme is always theoretically smaller than that of a previously published forwarder selection scheme under the same condition. In addition, we have shown that the proposed scheme reduces the energy consumption compared with the previous scheme.

Although Bitcoin is one of the most successful decentralized cryptocurrency, recent research has revealed that it can be used as fraudulent activities such as HYIP (High Yield Investment Program). To identify such undesired activities, it is important to obtain Bitcoin addresses related with fraud. So far, the identification of such activities is based upon relating Bitcoin addresses with graph mining procedures. In this paper, we follow a different approach for identifying Bitcoin addresses related with HYIP by analyzing transactions patterns. In particular, based on the individual inspection of HYIP activity in Bitcoin, we propose a number of features that can be extracted from transactions. In particular, a signed integer called pattern is assigned to each transaction and the frequency of each pattern is calculated as key features. By evaluating the classification performance with more than 1,500 labeled Bitcoin addresses, it is shown that about 83% of HYIP addresses are correctly classified while maintaining false positive rate less than 4.4%.

Bitcoin is a decentralized currency system that does not need any central authorities. All transactions issued by users have been recorded in the common ledger, called blockchain, which is shared by all users. In Bitcoin, an SPV (Simplified Payment Verification) client, which is a lightweight client that does not possess the entire blockchain, are developed for storage constrained devices such as a mobile phone. For an SPV client to check if there are transactions related to it, a Bloom filter where their Bitcoin addresses are involved is sent to a full client that possesses the entire blockchain. The full client only transfers transactions of which Bitcoin addresses are positive on the received Bloom filter. However, it is necessary to preserve the privacy of SPV clients when designing a Bloom filter because SPV clients' Bitcoin addresses will be identified by a full client with high probability. In this paper, we propose a privacy-preserving Bloom filter design for SPV clients based on γ-Deniability. γ-Deniability is a privacy metric that shows how much true positive Bitcoin addresses are hidden by the false positives in a Bloom filter. Furthermore, in order to design a Bloom Filter that satisfies a certain γ-Deniability, it is necessary to know the number of unique Bitcoin addresses that appear for the first time since the queried time. Based on our manual inspection, we propose to estimate it based on the linear regression. We show that our scheme achieves good estimation accuracy and γ through the simulation with a real Bitcoin blockchain.

Blink is a physiological signal that reflects drowsiness and concentration. It is important to detect driver's blinks without any wearable devices. For this purpose, a Doppler sensor has been used and several blink detection methods where a subject sits in front of such sensor have been proposed. However, it is challenging to detect driver's blinks because of face and body movement. In this paper, we propose a Doppler sensor-based driver's blink detection method in existence of face and body movement in a car. In the proposed method, blinks are detected through two steps: pre-detection and classification. In the first step which we call pre-detection, the time candidates of subject's blinks are detected based on spectrograms calculated from a received signal. Then, in the second one which we call classification, a set of features are calculated from a spectrogram and are fed into a supervised machine learning classifier to identify which time candidates are truly blinks. We leverage the fact that the distribution of the energy on a spectrogram differs between a blink and non-blink. Specifically, features are extracted based on the distribution of energy on a spectrogram. We conducted a series of experiments for the evaluation in the situation where a subject drives a real car in public road. As a result, we confirmed our method outperforms the conventional one in terms of F-measure calculated from recall rate and precision rate.

In RFID-enabled supply chains, in order to securely deliver products, a secret sharing scheme has been proposed. An encryption key of tags is split into shares and the key can be recovered with the sufficient number of correct shares. The conventional scheme introduces sufficient number of dummy tags with bogus shares and prevents an attacker who reads all tags during shipping from obtaining tag information. The number of required dummy tags is decided so that the success probability of the attack becomes lower than that in the brute force manner. When the number of shipping products is small, too many dummy tags are required to prevent the attacker from selecting correct shares with a small number of trial. In this paper, we propose a practical scheme which reduces the number of tags by using extra legitimate tags. An encryption key and its share for legitimate tags are assigned to not only the conventional legitimate tags but also extra legitimate tags, and thus, the number of legitimate tags is virtually increased. By doing this, the number of required dummy tags can be reduced. Extra legitimate tags are separately composed from conventional legitimate tags and dummy tags, then they can be distinguished easily. We show the effectiveness of our scheme by computer simulation.

In WRSNs (Wireless Rechargeable Sensor Networks), the wireless energy transfer technology enables charging sensor batteries and maintaining perpetual operation of the network. The MC (mobile charger) is able to provide stable and reliable energy supply for sensor nodes. Although many researchers study the efficient scheduling of the MC, none of them considers the situation where both the static sink and the MC can collect packets. In this case, each sensor node should choose the appropriate destination, i.e., the static sink or the MC. In this paper, we propose closer destination selection scheme for mobile sink and charger enabled WRSNs where a static sink and an MC coexist. In order for each sensor node to send packets to the closer destination, i.e., the static sink or the MC, when an MC tours to charge sensor nodes, it constructs another temporal tree-based topology network by sending a route construction packet to sensor nodes within the limited area. Whenever the MC stays at a sensor node, it calculates the maximum number of hops that sensor nodes should switch to a temporal network. This temporal network that the MC creates does not disturb the original network. By constructing a temporal network, an MC relaxes the load of sensor nodes around the static sink without unnecessary path extension. We evaluate the performance of our scheme through the computer simulation. Results indicate that our scheme improves the packet arrival rate and energy efficiency compared to the conventional scheme.

The video streaming service over Wireless Mesh Networks (WMNs) is an attractive feature for live streaming and video conversation when disaster occurs. The path construction with the conventional metrics e.g., throughput, latency, or packet loss rate, suffers from the repeated waiting time due to the CSMA/CA since paths are concentrated on nodes with good metric. As a consequence, it brings about throughput fluctuation of video streaming service. We propose two metrics that reflect on the magnitude of CSMA/CA interference around a node in order to select a path that the throughput of video streaming session will get stable. The first metric is calculated from the number of received Request To Send (RTS)/Clear To Send (CTS) frames. Since the number of RTS/CTS reflects on the frequency of the packet transmission around a node, a source node can chooses a path which does not make a node waiting for constant time due to the CSMA/CA on the existing path that might use streaming service. The second metric uses the number of neighbor nodes and we set the larger metric value as more nodes exist. The idea comes from the fact that a node which has few neighbor nodes does not much interfere other established paths. The proposed metrics can be easily installed into Hybrid Wireless Mesh Protocol (HWMP) which is a de facto standard path selection protocol defined in IEEE802.11s. We show that the proposed metrics can achieve lower rate of throughput fluctuation than that of conventional metric by using the networks simulator Qualnet.

The IPv6 Routing Protocol for Low-power and Lossy networks (RPL) is a standard routing protocol to realize the Internet of Things (IoT). Since RPL is a tree-based topology network, an attacking node may falsely claim its rank towards neighbor nodes in order to be chosen as a parent of them and to collect more packets to tamper. In this paper, we propose a secure parent selection scheme so that each child node can select a legitimate node as its parent. In the proposed scheme, each node chooses a parent after excluding the best candidate if multiple parent candidates exist. Our scheme utilizes the fact that an attacking node claims falsely a lower rank than that of a legitimate nodes. We show that attacking nodes have no merits to claim lower ranks than true ones in a secure parent node selection scheme. By the computer simulation, we show that the proposed scheme reduces the total number of child nodes attached to attacking nodes in comparison with the conventional RPL scheme.

Detecting SPIT (Spam over Internet Telephony) is an urgent demand with growing voice communication services. Chaisamran et al. proposed a trust-based SPIT detection scheme and it is superior to other trust-based schemes since it is valid for unknown users. However, this scheme might misdetect a call from low-frequent users as SPIT gradually in time, since they seldom call and thus their social trusts get decreased with time. In this paper, we propose a two-stage SPIT detection scheme using BC (Betweenness Centrality) and social trust to decrease misdetection of a call from low-frequent users as SPIT. BC indicates user's centrality in the entire network and the value of BC against legitimate users gradually increases with time even if users seldom call, whereas it does not increase against spammers since the connection between a legitimate caller and a spammer is hardly established. As a first stage, we use BC as a feature to correctly identify a call request from a low-frequent user. Then we judge whether a call is from a legitimate caller or a spammer by using social trust. By the computer simulation, we show that our scheme improves the false positive rate while maintaining high true positive rate.

In Wireless Sensor Networks (WSNs), Energy Harvesting (EH) is emerging as a promising solution to realize the energy-efficient green communications. Non-EH nodes can save the energy by using EH nodes as relaying nodes, and relaying with the better throughput is required to efficiently consume the precious energy. In most cases, the EH charging rate gets low since current charging techniques cannot convert the environmental energy well, so it is important to achieve high performance in low charging rate. In this paper, we propose an adaptive relay selection scheme based on the average charging rate to achieve better throughput even in low charging rate. In our scheme, each node adaptively changes the criterion to be a relaying candidate based on the current charging rate; an EH node whose charging rate is low raises the threshold to save the precious energy for better channel condition. By the computer simulation, we show that our proposed relay selection scheme achieves the better throughput than that of the conventional schemes in low charging rate, which is the more realistic condition.

With the recent growth of mobile communication, location-based services (LBSs) are getting much attention. While LBSs provide beneficial information about points of interest (POIs) such as restaurants or cafes near users, their current location could be revealed to the server. Lien et al. have recently proposed a privacy-preserving k-nearest neighbor (k-NN) search with additive homomorphic encryption. However, it requires heavy computation due to unnecessary multiplication in the encryption domain and this causes intolerant burden on the server. In this paper, we propose a lightweight private circular query protocol (LPCQP) for privacy-preserving k-NN search with additive and multiplicative homomorphism. Our proposed scheme divides a POI-table into some sub-tables and aggregates them with homomorphic cryptography in order to remove unnecessary POI information for the request user, and thus the computational cost required on the server is reduced. We evaluate the performance of our proposed scheme and show that our scheme reduces the computational cost on the LBS server while keeping high security and high accuracy.

As VoIP (Voice over IP) grows rapidly, it is expected to prevail tremendous unsolicited advertisement calls, which type of calls is referred to SPIT (SPam over Internet Telephony). SPIT detection is more difficult to execute than email SPAM detection since the callee or SPIT detection system does not tell whether it is SPIT or legitimate call until he/she actually takes a call. Recently, many SPIT detection techniques are proposed by finding outliers of call patterns. However, most of these techniques suffer from setting a threshold to distinguish that the caller is legitimate or not and this could cause to high false negative rate or low true positive rate. This is because these techniques analyse call pattern by a single feature e.g. call frequency or average call duration. In this paper, we propose a multi-feature call pattern analysis with unsupervised Random Forests classifier, which is one of the excellent classification algorithms. We also propose two simple but helpful features for better classification. We show the effectiveness of Random Forests based classification without supervised training data and which features contribute to classification.

In Mobile IPv6(MIPv6), a Mobile Node(MN) communicating with the Correspondent Node(CN) cannot prove the ownership of the claimed IP address. If a malicious node impersonates victim's IP address, it could hijack the session or forward packets to non-existing destination or other nodes. Currently, in order for a MN to prove the ownership of its own IP address, it is considered to use Feige-Fiat-Shamir (FFS) identification scheme [1]. However, there is one serious problem. In this scheme, a CN has to verify all of the Binding Update requests, and this leads to DoS(Denial of Service) attack. This paper shows a method which mitigates the effect of the DoS attack by making the challenge twice in a transaction. We make the first challenge easily verified to exclude the malicious nodes and the second one much more difficult than the first one to avoid impersonation. This method can efficiently exclude the malicious nodes which do not have proper IP addresses by verifying the first challenge. Furthermore, by making the challenge twice, our scheme can decrease the probability of impersonation over the previous scheme for equivalent calculation amount. By the computer simulation, we show that the proposed scheme is efficient to decrease the effect of the DoS attack and the probability of impersonation compared to the previous scheme.