Wireless body area networks (WBANs) and wireless sensor networks (WSNs) are important concepts for the Internet of Things (IoT). They have been applied to various healthcare services to ensure that users can access convenient medical services by exchanging physiological data between user and medical server. User physiological data is collected by sensor nodes and sent to medical service providers, doctors, etc. using public channels. However, these channels are vulnerable to various potential attacks, and hence, it is essential to design provably secure and lightweight mutual authentication (MA) schemes for medical IoT to protect user privacy and achieve secure communication. A lightweight mutual authentication and key agreement (MAKA) scheme was designed in 2019 to guarantee user privacy, but we found that the scheme does not withstand impersonation, stolen senor node and leaking verification table attacks, and it does not also ensure anonymity, untraceability and secure mutual authentication. This paper proposes a provably secure and lightweight MAKA scheme for medical IoT, called LAKS Non-verification table (NVT), that does not require a server verification table. We assess LAKS-NVT's security against various potential attacks and demonstrate that it achieves secure MA between sensor node and server using Burrows-Abadi-Needham logic. We employ the well-known Real-Or-Random which is random oracle model to prove that LAKS-NVT provides a session key security. In addition, the formal security verification using the widely-accepted Automated Validation of Internet Security Protocols and Applications (AVISPA) software tool has been performed and the results show that LAKS-NVT is also secure. We compare LAKS-NVT's performance against contemporary authentication schemes, and verify that it achieves better security and comparable efficiency. The practical perspective of LAKS-NVT is also carried out via the Network Simulator 2 (NS2) simulation study.

With the widespread of Internet of Things (IoT) environment, a big data concept has emerged to handle a large number of data generated by IoT devices. Moreover, since datadriven approaches now become important for business, IoT data markets have emerged, and IoT big data are exploited by major stakeholders such as data brokers and data service providers. Since many services and applications utilize data analytic methods with collected data from IoT devices, the conflict issues between privacy and data exploitation are raised, and the markets are mainly categorized as privacy protection markets and privacy valuation markets, respectively. Since these kinds of data value chains (which are mainly considered by business stakeholders) are revealed, data providers are interested in proper incentives in exchange for their privacy (i.e., privacy valuation) under their agreement. Therefore, this paper proposes a competitive data trading model that consists of data providers who weigh the value between privacy protection and valuation as well as other business stakeholders. Each data broker considers the willingness-to-sell of data providers, and a single data service provider considers the willingness-to-pay of service consumers. At the same time, multiple data brokers compete to sell their dataset to the data service provider as a non-cooperative game model. Based on the Nash Equilibrium analysis (NE) of the game, the feasibility is shown that the proposed model has the unique NE that maximizes the profits of business stakeholders while satisfying all market participants.

In order to monitor continuously moving phenomena such as wile fire and hazardous bio-chemical material in wireless sensor network, boundary tracking approach has been widely used by reason of its huge scale and extensive diffusion property. With the boundary tracking scheme, the energy efficiency is expected to improve if only sensor nodes near the boundary of continuous object actively participate in boundary tracking process, while other sensor nodes stay in sleep mode for energy saving. In this paper, we propose a predictive continuous object tracking scheme, which uses minimum set of active sensing nodes to reduce energy consumption. The proposed scheme predicts the future boundary line, which provides the knowledge for a wakeup mechanism to decide which sleeping nodes need to be activated for future tracking. The proposed algorithm is verified with simulation results that total energy consumption can be dramatically reduced under acceptable boundary detection accuracy. Index Terms-Continuous object and boundary tracking

In many applications of Wireless Sensor Networks (WSNs), the sensing data are disseminated from a source to multiple mobile sinks. Since WSNs consists of a number of sensor nodes with limited capabilities, previous studies mainly discuss on how to send the data efficiently and do not consider the group mobility of mobile sinks that move together staying closely and randomly move within a geographically restricted region. Although the existing multicasting protocols could be applied, they suffer from high congestion and control overhead due to location updates by individual mobile sinks. Geocasting protocols are effective for data delivery to a sink group within a restricted region, but do not guarantee since they only focus on transmitting data to all nodes within a stationary region. Therefore, we propose Region Based Data Dissemination (RBDD) scheme to address these problems. RBDD provides efficient data dissemination scheme for mobile sink groups, so it guarantees data transmission not only when a sink group does move as a whole, but also its member sinks move inside of the region or outside of it. Simulation results show that RBDD guarantees data delivery to a mobile sink group.

No abstract