Efficient Anonymous Authentication Scheme in Body Area Networks Via Signal Propagation Characterization

Owing to its capability to measure the sensitive biological data of patients through embedded sensors and transmit them via open wireless channels to remote medical experts, wireless body area network (WBAN) has been playing an important role in pervasive healthcare systems. However, the open nature of the wireless channels renders the data susceptible to being eavesdropped by an adversary and linked to the identities of the transmitting devices, which can enable the adversary to gain sensitive information and launch targeted physical attacks. Therefore, anonymous authentication and confidentiality of the data in WBAN are vital. In the last few years, numerous anonymous authentication schemes based on cryptographic primitives and physiological features were designed to enhance security in WBAN. However, most of the existing schemes are not computationally efficient or require additional sensing hardware. To address these limitations, we propose an efficient anonymous authentication scheme for WBAN based on signal propagation characteristics. The key idea in the proposed scheme is to utilize the distinct received signal strength (RSS) variation profiles between on-body and off-body communication channels to conceal the identities of communicating devices, thereby ensuring their anonymity during authentication. We perform security and performance analyses of the proposed approach to prove its security strength and computational efficiency, respectively. Moreover, extensive experiments are conducted on human volunteers in indoor and outdoor environments to show the robustness of our approach. The results of the analyses and the experiments show that our scheme can successfully mitigate 88.8% of active attack attempts with less computation overhead.

Detection of Distributed Denial of Service Flooding Attack Using Odds Ratio

Computer networks are vulnerable to many types of attacks while the Distributed Denial of Service attack (DDoS) serves as one of the top concerns for security professionals. The DDoS flooding attack denies the services by consuming the server resources to prevent the legitimate users from using their desired services. The hardness of detecting this attack lies in sending a stream of packets to the server with spoofed IP addresses, so that the internet routing infrastructure cannot distinguish the spoofed packets. Based on the odds ratio (OR) statistical measurement, in this work we propose a new detection method for the DDoS flooding attacks. By exploring the odds ratio to determine the risk factor of any incoming traffic to the server, the legitimate and attack traffic packets can be easily differentiated. Experimental results demonstrate the efficiency of the presented detection method in terms of its detection probability and detection time.

Efficient Beacon Deployment for Large-scale Positioning

Instant and precise localization of indoor mobile users is fundamental for supporting various sophisticated location-aware services. Using Bluetooth low-power beacons for mobile user positioning has been reported as an effective approach, where the beacon deployment positioning (BDP) problem has been defined. The paper introduces a novel approach for solving large-scale BDP problems, aiming to significantly reduce beacon consumption from existing solutions with much less computation complexity. Extensive simulations are conducted to verify the proposed algorithm, whose beacon consumption is about 1.14 to 1.67 times and 0.2 to 0.48 times compared to those of the Mixed Integer Linear Program (ILP) and a naive iBeacon solution respectively. We have also observed that the running time scales well with the growth of the number of Test Positions and attenuation factors.

Consortium Blockchain based Reputation Incentive Mechanism for Recommendation System

Recommendation systems have been widely used in many e-commerce services, but it is difficult to gather enough participants to supply their recommendations. Moreover, participants in the system may make malicious recommendations, which will affect the accuracy of recommendation results. In order to provide better recommendation service for users, incentive mechanisms are needed to attract more participants in recommendation and curb their malicious behaviors. In this paper, we propose a consortium blockchain based reputation incentive mechanism for recommendation systems(CRIM). Firstly, the monetary rewards are used to attract participants and motivate them to take part in the recommendation. Secondly, we design the incentive mechanism with reputation which is attached to the rewards. Honest participants will gain more rewards while malicious participants will be penalized. Meanwhile, we adopt the Stackelberg game to maximize the utility of participants, and prove that the mechanism can reach a unique Nash equilibrium. Thirdly, the decentralization and immutability of blockchain can guarantee the credibility and security of the stored data, thus ensuring the openness and transparency of the recommendation. Finally, we implement the system for education resources recommendation and conduct experiments, and the results demonstrate that our incentive mechanism is effective and has significant performance when compared with other incentive mechanisms.

Performance Analysis of Maximum Likelihood Estimation for Multiple Transmitter Locations

The paper considers the consistence condition of Maximum Likelihood (ML) estimation for multiple transmitter locations in a wireless network with cooperative receiver nodes. It is found that the location set of receiver nodes should not locate (or asymptotically in some sense) merely in an algebraic curve of order 2M −1 if there are totally M transmitters. A sufficient condition for consistence of the ML estimation for M transmitters is that the limit set of locations contains a subset, comprised of (2M2 −M +2) points, which is non-C-2M-co-curved, a notion given by Definition IV-B. This condition can be compared to the persistent excitation condition used to guarantee the convergence of least squares algorithm. Numerical experiments are designed to demonstrate the theoretical discoveries in both positive and negative aspects.

k-Anonymous Query Scheme on the Internet of Things: a Zero Trust Architecture

The paper investigates query-anonymity in Internet of things (IoT) formed by a sensor cloud, where the sensor nodes provide services of sensing and are subject to user queries of sensing data. Due to the heterogeneity and multi-carrier natures of the sensor cloud, user privacy could be impaired when the queries have to go through nodes of a third party. Thus, the paper firstly introduces a novel query k-anonymity scheme that countermeasures such a privacy threat. Based on the proposed k-anonymity scheme, the trade-offs between the achieved query-anonymity and various performance measures including, communication-cost, return-on-investment metric, path-length, and location anonymity metrics, are analyzed. By adopting a hybrid approach that takes into account the average and worst-case analysis, our evaluation results show that most of the obtained bounds on various performance anonymity trade-offs can be expressed precisely in terms of the offered level-of-anonymity k and network diameter d.

Embedded Contact Tracing using Mobile Devices, Cloud, and iBeacons

Covid-19 is a modern pandemic that has ripped through the fabric of our daily lives. We are required to limit our exposure to other people to help safeguard each other’s health. Contact-tracing/early detection serves as an effective solution for the management of Covid-19 and other similar pandemic diseases. The current popular IoT contact tracing implementations rely on a mobile-centric approach where cellular phones broadcast as beacons and nearby embedded devices log beacon interaction data locally. The proposed approach is a hybrid method that utilizes decentralized iBeacons to track individuals and a centralized cloud infrastructure to communicate/store user exposure data with BLE and Cloud access. The solution addresses potential privacy concerns and offers a simple low-cost contact tracing infrastructure set-up for offices, s chools, a nd o ther s imilar p ublic spaces.

Multi-Pair Cable Measurements for 5G Indoor Service Provisioning

This paper considers the 5G new radio (NR) indoor service provisioning scenario where the CPRI link terminates at remote radio unit (RRU) in the building and multi-pair cable, i.e. CAT-5, is used to provide connectivity between distributed antenna unit (DAU) and RRU. The paper focuses on the de-tailed methodology for multi-pair copper channel measurement including measurement equipment specifications, their respective settings, measurement parameters i.e. characteristics impedance, insertion loss (IL), far-end-crosstalk (FEXT) and near-end-crosstalk (NEXT). The measurements include the cables for next-generation multi-pair system, where multi-pair cables, i.e. CAT-5 cables, are used from the RRU to the DAUs in a building. Conventionally, many multi-pair cables share the same duct in a building and interfere with each other, therefore two parallel multi-pair CAT-5 cables of 50m each are considered for the measurement scenario. This setup result in 4 twisted pairs terminate at each DAU and this configuration is termed as next-generation multi-pair MIMO (NGMM). The number of twisted pairs is increased by using the Phantom mode circuit connected to the opposite ends of each CAT-5 cable and the resulting configuration is called next-generation multi-pair super MIMO (NGMSM). The cable parameters have been measured for frequencies up to 1 GHz and 250 MHz for NGMM and NGMSM, respectively. DL data rate of each configuration is examined by applying conventional block diagonalization.