Fog computing has been proposed as an extension of cloud computing to provide computation, storage, and network services in network edge. For smart manufacturing, fog computing can provide a wealth of computational and storage services, such as fault detection and state analysis of devices in assembly lines, if the middle layer between the industrial cloud and the terminal device is considered. However, limited resources and low-delay services hinder the application of new virtualization technologies in the task scheduling and resource management of fog computing. Thus, we build a new task-scheduling model by considering the role of containers. Then, we construct a task-scheduling algorithm to ensure that the tasks are completed on time and the number of concurrent tasks for the fog node is optimized. Finally, we propose a reallocation mechanism to reduce task delays in accordance with the characteristics of the containers. The results showed that our proposed task-scheduling algorithm and reallocation scheme can effectively reduce task delays and improve the concurrency number of the tasks in fog nodes.

Tasks Scheduling and Resource Allocation in Fog Computing Based on Containers for Smart Manufacturing

The Internet of Things (IoT)-Cloud combines the IoT and cloud computing, which not only enhances the IoT’s capability but also expands the scope of its applications. However, it exhibits significant security and efficiency problems that must be solved. Internal attacks account for a large fraction of the associated security problems, however, traditional security strategies are not capable of addressing these attacks effectively. Moreover, as repeated/similar service requirements become greater in number, the efficiency of IoT-Cloud services is seriously affected. In this paper, a novel architecture that integrates a trust evaluation mechanism and service template with a balance dynamics based on cloud and edge computing is proposed to overcome these problems. In this architecture, the edge network and the edge platform are designed in such a way as to reduce resource consumption and ensure the extensibility of trust evaluation mechanism, respectively. To improve the efficiency of IoT-Cloud services, the service parameter template is established in the cloud and the service parsing template is established in the edge platform. Moreover, the edge network can assist the edge platform in establishing service parsing templates based on the trust evaluation mechanism and meet special service requirements. The experimental results illustrate that this edge-based architecture can improve both the security and efficiency of IoT-Cloud systems.

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A Secure IoT Service Architecture With an Efficient Balance Dynamics Based on Cloud and Edge Computing

Vehicular energy network (VEN), as an important part of the Internet of Things for the smart city, can facilitate the renewable energy (RE) transportation over a large geographical area by means of electric vehicles (EVs) through wireless power transfer technology. However, due to the potential security vulnerability in VEN, EV users can be attacked by external or internal adversaries. In addition, owing to the selfishness of EVs, it is a great challenge to optimally schedule the charging/discharging behaviors of EVs to realize regional energy balance in VEN. To tackle the above issues, this paper proposes a blockchain-based secure incentive scheme for energy delivery in VEN. First, a novel permissioned energy blockchain system is introduced in VEN to implement secure energy delivery services for EVs and energy nodes through the use of distributed ledgers and cryptocurrency. Second, a proof of reputation consensus protocol is proposed to efficiently reach consensus in energy blockchain, where the reputation derivation is constructed based on the local trust computing and credibility computing. Third, motivated by the pricing mechanism, an incentive model is developed to stimulate EVs to cooperatively deliver RE to various areas with different electricity loads while maximizing EVs’ utilities. Finally, extensive numerical results are provided, which demonstrate the efficiency of the proposed scheme through the comparison with conventional schemes.

BSIS: Blockchain-Based Secure Incentive Scheme for Energy Delivery in Vehicular Energy Network

A caching and spatial K-anonymity driven privacy enhancement scheme in continuous location-based services

Internet of Things (IoT) applications have been growing significantly in recent years, however, the security issue has not been well studied in the literature for the IoT ecosystem. The sinkhole attack is one of serious destructive attacks for IoT as it is easy to launch the attack and difficult to defend it. In this paper, a Probe Route based Defense Sinkhole Attack (PRDSA) scheme is proposed to resist Sinkhole attack and guarantee security for IoT, which is the first work that can detect, bypass and locate the sinkhole at the same time. The PRDSA scheme proposes a routing mechanism combining the far-sink reverse routing, equal-hop routing, and minimum hop routing, which can effectively circumvent the sinkhole attacks and find a safe route to the real sink, so that the scheme can achieve better sinkhole detection. More importantly, the PRDSA scheme overcomes the limitation of previous schemes that they cannot locate the sinkhole. During the detection of the sinkhole attack, the PRDSA scheme requires the nodes and the sink node to return the signature of the information (e.g., IDs, etc.), so that the location of the sinkhole can be determined. Furthermore, the PRDAS scheme mainly utilizes the characteristics of network energy consumption. The probe route of sinkhole attack mainly occurs in the region where the remaining energy exists. Thus, the PRDSA scheme has little impact on the network lifetime. Theory and experiments show that this scheme can achieve better performance than existing schemes in term of network security and lifetime.

Design and Analysis of Probing Route to Defense Sink-Hole Attacks for Internet of Things Security

Real-time surveillance systems with green wireless sensor networks (WSNs) are vital for maintaining high energy efficiency in many situations. This paper considers a scenario utilizing green WSNs to monitor the situation of Internet of Things (IoT), which constitute one of the most crucial sources of electricity consumption in information and communications technologies (ICT). More specifically, we focus on optimizing the cluster structure to minimize the delay and energy consumption for aggregation convergecast in green WSNs. We first find the optimal value of the network cluster radius for minimizing the delay through theoretical analysis. We then propose a novel cluster network architecture in which clusters that are far from the sink are small, allowing inter-cluster data aggregation to be processed earlier, and clusters that are near the sink are relatively large to allow more time for intra-cluster data aggregation. Hence, the sensor nodes can be scheduled in consecutive time slots to reduce the number of state transitions, consequently achieving the goal of minimizing both delay and energy consumption. Simulation results indicate that the proposed Algorithm outperforms previously reported solutions in terms of both schedule length and lifetime, thereby demonstrating its effectiveness.

Minimizing Convergecast Time and Energy Consumption in Green Internet of Things

For wireless networks in the Internet of Things (IoT), cognitive radio (CR) is a promising way to obtain the available spectrum for objects Wide-band spectrum sensing plays an important role in building such CR networks of IoT In this paper, we propose a novel dynamic compressive wide-band spectrum sensing method based on channel energy reconstruction After a bank of wide-band random filters is employed to measure the channel energy, rather than to recover all the channel energy in the whole spectrum, only the channel energy with a changing occupancy status in consecutive time slots is recovered Furthermore, it is unnecessary to use reconstruction algorithm unless there are two or more channels changing their occupancy status Compared to the existing methods, our proposed schemes bear significant improvements in the probability of detection and reduction of probability of false alarms Simulation results also show its fast speed and robustness to noise

Dynamic Compressive Wide-Band Spectrum Sensing Based on Channel Energy Reconstruction in Cognitive Internet of Things

Due to its inherent ability in reducing hardware cost and power consumption while maintaining high system capacity, hybrid precoding is deemed as one of the key technologies in the upcoming 5G/6G millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) systems. However, it is challenging to design high performance hybrid precoders/combiners with low computational complexity. In this paper, based on the singular value decomposition (SVD) technique and the concept of equivalent channel, joint hybrid precoding strategies with high spectral-efficiency and low complexity are proposed for both single-user and multi-user massive MIMO systems. Specifically, for single-user massive MIMO scenarios, after transforming the design of hybrid beamforming into the problem of maximizing the square of sum eigenvalues for an equivalent channel, a two-stage successive method is conceived to design the analog precoder and combiner jointly, and the corresponding equivalent channel is constructed. Then, the digital precoding and combining operations are realized directly by applying the SVD technique to the matrix of equivalent channel. Meanwhile, the hybrid precoding strategy is extended to the multi-user scenario for achieving high performance resultant from multi-user diversity. Extensive simulations are conducted to verify the effectiveness of the precoding/combing schemes. The results show that our proposed schemes can achieve superior performance with lower complexity compared to the existing ones.

A Joint Hybrid Precoding/Combining Scheme Based on Equivalent Channel for Massive MIMO Systems

Pilot spoofing detection for massive MIMO mmWave communication systems with a cooperative relay

In wireless communication, unmanned-aerial-vehicle (UAV) relay seems to be an available paradigm to combat path-loss and expand signal coverage. Due to the openness of wireless channel and the inconvenient recharging, transmission security and energy efficiency are the most concerned issues for UAV-enabled communication. In this paper, for an UAV-enabled two-way relay system, where two wireless terminals exchange their information assisted by a flying UAV and a malicious user tries to eavesdrop the transmitted information, the achievable secrecy energy-efficiency is formulated when the UAV speed, trajectory, allowable power and the time scheduling in the uplink and downlink transmission are considered in a comprehensive manner. To solve this non-convex problem, we decompose it into three subproblems based on the block coordinate descent (BCD) method. Then, the successive convex approximation (SCA) technique and the Dinkelbach algorithm are combined to obtain the global solution in an iterative manner. Simulation results show that the proposed schemes have higher secrecy energy-efficiency compared to the existing baseline schemes. 

Shiguo Wang

Papers

Minimizing Convergecast Time and Energy Consumption in Green Internet of Things

Dynamic Compressive Wide-Band Spectrum Sensing Based on Channel Energy Reconstruction in Cognitive Internet of Things

A Joint Hybrid Precoding/Combining Scheme Based on Equivalent Channel for Massive MIMO Systems

Pilot spoofing detection for massive MIMO mmWave communication systems with a cooperative relay

Secrecy-Energy-Efficiency UAV-Enabled Two-Way Maximization for Relay Systems