Caching in Energy Harvesting Aided Internet of Things: A Game-Theoretic Approach

Yao, Jingjing; Ansari, Nirwan

doi:10.1109/jiot.2018.2880483

Cited by 67 publications

(44 citation statements)

References 25 publications

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“…Incentive mechanism design has been extensively studied for networking problems, such as caching [14], [15], traffic/computation offloading [16], [17], cooperative communications [18], etc. However, none of them consider the UAV-assisted application scenarios.…”

Section: B Related Workmentioning

confidence: 99%

Blockchain-Based Secure Spectrum Trading for Unmanned-Aerial-Vehicle-Assisted Cellular Networks: An Operator’s Perspective

Qiu

Grace

Ding

et al. 2020

IEEE Internet Things J.

165

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Unmanned aerial vehicles (UAVs) are envisioned to be widely deployed as an integral component in the next generation cellular networks, where spectrum sharing between the aerial and terrestrial communication systems will play an important role. However, there exist significant security and privacy challenges due to the untrusted broadcast features and wireless transmission of the UAV networks. This paper endeavors to resolve the security issues through proposing a novel privacypreserving secure spectrum trading and sharing scheme based on blockchain technology. Specifically, from the operator's perspective, a pricing-based incentive mechanism is firstly introduced, in which a primary mobile network operator (MNO) leases its owned spectrum to a secondary UAV network in exchange for some revenue from the UAV operators. To address the potential security issues, a spectrum blockchain framework is then proposed to illustrate detailed operations of how the blockchain helps to improve the spectrum trading environment. Under this framework, a Stackelberg game is formulated to jointly maximize the profits of the MNO and the UAV operators considering uniform and non-uniform pricing schemes. Security assessment and numerical results confirm the security and efficiency of our schemes for spectrum sharing in UAV-assisted cellular networks.

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Section: B Related Workmentioning

confidence: 99%

Blockchain-Based Secure Spectrum Trading for Unmanned-Aerial-Vehicle-Assisted Cellular Networks: An Operator’s Perspective

Qiu

Grace

Ding

et al. 2020

IEEE Internet Things J.

165

View full text Add to dashboard Cite

show abstract

“…Caching frequently requested information at the network edge is an effective measure to reduce the duplicate content transmission [20], [21]. The joint utilization of MEC and caching can significantly reduce the energy requirement [4] and task execution time [3].…”

Section: Collaboration Of Mec and Cachingmentioning

confidence: 99%

“…The repeatedly requested tasks are expected to be generated from diverse IoT applications including smart vehicles, e-health services, interactive gaming, smart homes, industrial monitoring, and virtual reality applications [17]- [19]. Caching popular IoT data items at the network edge can play an important role in reducing the duplicate content transmission [3], [20]. Edge caching significantly improves the efficiency of the content delivery and task offloading by reducing not only the latency, but also the energy consumption [4], [21].…”

Section: Introductionmentioning

confidence: 99%

Green and Secure Computation Offloading for Cache-Enabled IoT Networks

et al. 2020

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The ever-increasing number of diverse and computation-intensive Internet of things (IoT) applications is bringing phenomenal growth in global Internet traffic. Mobile devices with limited resource capacity (i.e., computation and storage resources) and battery lifetime are experiencing technical challenges to satisfy the task requirements. Mobile edge computing (MEC) integrated with IoT applications offloads computation-intensive tasks to the MEC servers at the network edge. This technique shows remarkable potential in reducing energy consumption and delay. Furthermore, caching popular task input data at the edge servers reduces duplicate content transmission, which eventually saves associated energy and time. However, the offloaded tasks are exposed to multiple users and vulnerable to malicious attacks and eavesdropping. Therefore, the assignment of security services to the offloaded tasks is a major requirement to ensure confidentiality and privacy. In this article, we propose a green and secure MEC technique combining caching, cooperative task offloading, and security service assignment for IoT networks. The study not only investigates the synergy between energy and security issues, but also offloads IoT tasks to the edge servers without violating delay requirements. A resource-constrained optimization model is formulated, which minimizes the overall cost combining energy consumption and probable security-breach cost. We also develop a two-stage heuristic algorithm and find an acceptable solution in polynomial time. Simulation results prove that the proposed technique achieves notable improvement over other existing strategies. INDEX TERMS Caching, energy, IoT, mobile edge computing, security. DARYOUSH HABIBI (Senior Member, IEEE) received the B.E. degree (Hons.

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“…In recent years, some researches use game theory to design charging strategies [17,18]. In literature [17], a game collaborative scheduling algorithm was proposed with the introduction of the unique dynamic warning threshold and sacrifice-charge mechanism.…”

Section: Related Workmentioning

confidence: 99%

“…The overall energy efficiency of the system was improved by solving Nash equilibrium. Literature [18] adopted the non-cooperative Stackelberg game model and realizes a new architecture with better performance than cache architecture and energy recovery architecture.…”

Section: Related Workmentioning

confidence: 99%

Joint Optimization Strategy of Coverage Planning and Energy Scheduling for Wireless Rechargeable Sensor Networks

Gong¹,

Guo²,

Xu³

et al. 2020

Preprint

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Wireless Sensor Networks (WSNs) has the characteristics of large-scale deployment, flexible networking, and wide application. It is an important part of the wireless communication networks. However, due to limited energy supply, the development of WSN is greatly restricted. Wireless Rechargeable Sensor Networks (WRSNs) transform the distributed energy around the environment into usable electricity through energy collection technology. In this work, a joint optimization strategy is proposed to improve the energy management efficiency for WRSNs. The joint optimization strategy is divided into two phases. In the first phase, we design an Annulus Virtual Force based Particle Swarm Optimization (AVFPSO) algorithm for area coverage planing. It adopts the multi-parameter joint optimization method to improve the efficiency of the algorithm. In the second phase, a Queuing Game-based Energy Supply (QGES) algorithm is designed for energy scheduling. It converts energy supply and consumption into network service. By solving the game equilibrium of the model, the optimal energy distribution strategy can be obtained. The simulation results show that our scheme improves the efficiency of coverage and energy, and extends the lifetime of WSN.

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Caching in Energy Harvesting Aided Internet of Things: A Game-Theoretic Approach

Cited by 67 publications

References 25 publications

Blockchain-Based Secure Spectrum Trading for Unmanned-Aerial-Vehicle-Assisted Cellular Networks: An Operator’s Perspective

Blockchain-Based Secure Spectrum Trading for Unmanned-Aerial-Vehicle-Assisted Cellular Networks: An Operator’s Perspective

Green and Secure Computation Offloading for Cache-Enabled IoT Networks

Joint Optimization Strategy of Coverage Planning and Energy Scheduling for Wireless Rechargeable Sensor Networks

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