Mithun Mukherjee

Security and Privacy in Fog Computing: Challenges

Fog computing paradigm extends the storage, networking, and computing facilities of the cloud computing toward the edge of the networks while offloading the cloud data centers and reducing service latency to the end users. However, the characteristics of fog computing arise new security and privacy challenges. The existing security and privacy measurements for cloud computing cannot be directly applied to the fog computing due to its features, such as mobility, heterogeneity, and large-scale geo-distribution. This paper provides an overview of existing security and privacy concerns, particularly for the fog computing. Afterward, this survey highlights ongoing research effort, open challenges, and research trends in privacy and security issues for fog computing.

Reduced out-of-band radiation-based filter optimization for UFMC systems in 5G

Universal-filtered multi-carrier (UFMC) technique is considered as a potential candidate for future communication systems due to its robustness against inter-carrier interference (ICI), suitability for non-contiguous fragmented available spectrum resources and low latency scenario in 5G network. In this paper, we present a novel pulse shaping approach in UFMC to reduce the spectral leakage into nearby subbands used for same or other users with low complexity and high throughput. In the new scheme, we apply Bohman filter-based pulse shaping with combination of antipodal symbol-pairs to the edge-subcarriers of the subbands, and consequently reduce the out-of-band radiation. This scheme outperforms the current state-of-the art and offers better signal-to-interference ratio (SIR) to improve the robustness against carrier frequency offset (CFO) for energy saving in loosely synchronized scenario. We further validate the proposed scheme on field programmable gate array (FPGA) hardware prototype.

Toxic gas boundary area detection in large-scale petrochemical plants with industrial wireless sensor networks

Industrial WSNs are evolving to become the key interconnection between management and factory products in large-scale petrochemical plants. Apart from improved manufacturing, asset tracking, and robotic applications, toxic gas detection is one of the major issues in petrochemical plants, since toxic gas leakage can severely threaten the safety of first-line working staff. Continuous object detection, one of the major applications in WSNs, has become an important research topic in large-scale industry. This article overviews continuous object detection techniques that have emerged in recent years. Most of the research focuses on the estimation of the toxic gas boundary. However, an accurate boundary is less likely to be detected due to the nature (e.g., invisibility, fast movement, and changing shape) of toxic gas. Thus, it is essential to ensure the boundary area rather than only the boundary of the toxic gas. We propose a novel boundary area detection technique with planarization algorithms like RNG and GG. Exhaustive simulation studies enable us to find an optimal trade-off point between the cost of a number of deployed sensor nodes and the accuracy of the estimated toxic gas boundary area size.

Smart Factory of Industry 4.0: Key Technologies, Application Case, and Challenges

Due to the current structure of digital factory, it is necessary to build the smart factory to upgrade the manufacturing industry. Smart factory adopts the combination of physical technology and cyber technology and deeply integrates previously independent discrete systems making the involved technologies more complex and precise than they are now. In this paper, a hierarchical architecture of the smart factory was proposed first, and then the key technologies were analyzed from the aspects of the physical resource layer, the network layer, and the data application layer. In addition, we discussed the major issues and potential solutions to key emerging technologies, such as Internet of Things (IoT), big data, and cloud computing, which are embedded in the manufacturing process. Finally, a candy packing line was used to verify the key technologies of smart factory, which showed that the overall equipment effectiveness of the equipment is significantly improved.

A Survey on Gas Leakage Source Detection and Boundary Tracking with Wireless Sensor Networks

Gas leakage source detection and boundary tracking of continuous objects have received a significant research attention in the academic as well as the industries due to the loss and damage caused by toxic gas leakage in large-scale petrochemical plants. With the advance and rapid adoption of wireless sensor networks (WSNs) in the last decades, source localization and boundary estimation have became the priority of research works. In addition, an accurate boundary estimation is a critical issue due to the fast movement, changing shape, and invisibility of the gas leakage compared with the other single object detections. We present various gas diffusion models used in the literature that offer the effective computational approaches to measure the gas concentrations in the large area. In this paper, we compare the continuous object localization and boundary detection schemes with respect to complexity, energy consumption, and estimation accuracy. Moreover, this paper presents the research directions for existing and future gas leakage source localization and boundary estimation schemes with WSNs.

Sleep Scheduling in Industrial Wireless Sensor Networks for Toxic Gas Monitoring

Toxic gas leakage that leads to equipment damage, environmental effects, and injuries to humans is the key concern in large-scale industries, particularly in petrochemical plants. Industrial wireless sensor networks (IWSNs) are specially designed for industrial applications with improved efficiency, and remote sensing for toxic gas leakage. Sleep scheduling is a common approach in IWSNs to overcome the network lifetime problem due to energy constrained nodes. In this article, we propose a sleep scheduling scheme that ensures a coverage degree requirement based on the dangerous levels of the toxic gas leakage area, while maintaining global network connectivity with minimal awake nodes. Unlike the previous sleep scheduling algorithm, for example, the connected k-neighborhood (CKN)-based approach that wakes up the sleep nodes over the entire sensing field by increasing the k-value, our proposed scheme dynamically wakes up the sleep nodes only in the particular toxic gas leakage area. Simulation results show that our proposed scheme outperforms the CKN-based sleep scheduling scheme with the same required coverage degree for the toxic gas leakage area. In addition, the proposed scheme considers multiple hazardous zones with various coverage degree requirements. We show that at the expense of a slight extra message overhead, energy consumption in terms of totally awake nodes over the entire sensing field is reduced compared to the other approaches, while maintaining network connectivity.

Internet of Things for Disaster Management: State-of-the-Art and Prospects

Disastrous events are cordially involved with the momentum of nature. As such mishaps have been showing off own mastery, situations have gone beyond the control of human resistive mechanisms far ago. Fortunately, several technologies are in service to gain affirmative knowledge and analysis of a disaster’s occurrence. Recently, Internet of Things (IoT) paradigm has opened a promising door toward catering of multitude problems related to agriculture, industry, security, and medicine due to its attractive features, such as heterogeneity, interoperability, light-weight, and flexibility. This paper surveys existing approaches to encounter the relevant issues with disasters, such as early warning, notification, data analytics, knowledge aggregation, remote monitoring, real-time analytics, and victim localization. Simultaneous interventions with IoT are also given utmost importance while presenting these facts. A comprehensive discussion on the state-of-the-art scenarios to handle disastrous events is presented. Furthermore, IoT-supported protocols and market-ready deployable products are summarized to address these issues. Finally, this survey highlights open challenges and research trends in IoT-enabled disaster management systems.

Geographic routing in duty-cycled industrial wireless sensor networks with radio irregularity

Industrial wireless sensor networks (IWSNs) are required to provide highly reliable and real-time transmission. Moreover, for connected K-neighborhood (CKN) sleep scheduling-based duty-cycled IWSNs in which the network lifetime of IWSNs can be prolonged, the two-phase geographic greedy forwarding (TPGF) geographic routing algorithm has attracted attention due to its unique transmission features: multi path, shortest path, and hole bypassing. However, the performance of TPGF in CKN-based duty-cycled IWSNs with radio irregularity is not well investigated in the literature. In this paper, we first evaluate the impact of radio irregularity on CKN-based duty-cycled IWSNs. Furthermore, we investigate the routing performance of TPGF in CKN-based duty-cycled IWSNs with radio irregularity, in terms of the number of explored routing paths as well as the lengths of the average and shortest routing paths. Particularly, we establish the upper bound on the number of explored routing paths. The upper bound is slightly relaxed with radio irregularity compared with without radio irregularity; however, it is bounded by the number of average 1-hop neighbors in always-on IWSNs. With extensive simulations, we observe that the cross-layer optimized version of TPGF (i.e., TPFGPlus) finds reliable transmission paths with low end-to-end delay, even in CKN-based duty-cycled IWSNs with radio irregularity.

Energy utilization concerned sleep scheduling in Wireless Powered Communication Networks

Wireless Powered Communication Networks (WPCN) is one of the promising approaches to extend the lifetime of the energy-constrained wireless networks such as Wireless Sensor Networks (WSNs). With the advancement in energy harvesting in terms of Wireless Energy Transmission (WET), WPCN overcomes the problem of replacing fixed energy sources such as batteries in difficult access areas of industrial networks. However, residual energy along with harvested energy is not always enough to support the data transmission in IWSNs. This article introduces a energy utilization concerned sleep scheduling in WPCNs with an aim to balance network demands and residual energy with harvested energy. In the proposed scheme, when the harvested energy combined with residual energy is less than the energy consumption due to data transmission, then the sensor node goes to sleep-state in order to prevent death acceleration. As the sleep node obtains enough energy, this node goes to active-state in next epoch if the network demand increases. Finally, a trade-off between the time to harvesting energy and data transmission is obtained through extensive simulation.

Energy-Efficient Event Determination in Underwater WSNs Leveraging Practical Data Prediction

Underwater environments may vary gradually even when the occurrence of events is detected. Sensory data may follow a certain trend and are predictable during certain time durations. Taking these into consideration, a simple but practical data prediction mechanism is adopted for estimating sensory data and the geographical location of sensor nodes at sink nodes, and these data are synchronized with those sensed by underwater sensor nodes only when their variation is beyond a prespecified threshold. Leveraging these predicted data, the coverage and sources of potential events are identified by the sink node, and the evolution of these events is determined accordingly. Evaluation results show the applicability and energy-efficiency of this approach, especially when the variation of network environments follows certain and simple patterns.