Internet of Things Aware Secure Dew Computing Architecture for Distributed Hotspot Network: A Conceptual Study
Abstract
:1. Introduction
- Using IoT devices as sensor data generators under the supervision of nearby hotspots that may be unknown to these IoT devices.
- The architecture presented here is a distributed hotspot architecture that can be assimilated with many IoT devices to scale the data generation process.
- The presented architecture can leverage highly secure and authenticated hotspot network coverage by using blockchain.
- The whole concept behind the architecture is to use a decentralized approach for the connected hotspot devices that can act as distributed gateways.
- Such hotspots can earn crypto tokens as rewards for certain activities such as data transmission, coverage stability, validation, and mining.
- Authentication and security-enabled hotspot networks can span several miles without requiring existing cellular networks.
- Dew computing is integrated into hotspot devices to provide increased computing power and capacity that are independent of the Internet backhaul.
- By deploying dew servers in the hotspots, they can be configured to become full-fledged, lightweight, data-only hotspots.
- The hotspots that are presented here can perform mining, validation, IoT data coverage, and crypto token collection from the blockchain.
- Remote users and application servers can be connected to such secure, authentication-enabled incentive hotspot networks, through which distributed IoT devices can transmit sensor data over long distances.
- In this design, IoT devices can be equipped with Wi-Fi and/or LoraWAN antenna modules that can be reasonably deployed according to the data transmission needs (low or high) and distance requirements.
- The presented architecture can open a new business model where hotspot owners can earn crypto tokens from their hotspots to reduce subscription and operational costs and make the whole network ecosystem sustainable.
2. Related Work
Our Key Contributions
- We use a dew computing paradigm that can provide independence on the integrated hotspot nodes. Doing so, our architecture can work on a rental basis where an actual hotspot network can be formed in a purely distributed manner. Dew computing uses high reliable synchronization techniques that help the connected devices to use the network even when a given dew system is not able to process the connected device’s request. Dew computing can form dewlets that support the rental facility of the network coverage from nearby hotspots to enrich the availability and overall quality of the service.
- Dew server-based hotspots can act as miner and validators to facilitate the generation of the reliability factors of the blockchain network. Based on the PoC challenge, once a hotspot miner or validator solves the challenge, it can establish the reliability. The hotspot miner, upon completion of certain PoC and data transmission activities, can earn crypto token which can be reflected in the wallets of the respective hotspot owners.
- Our architecture uses IoT-based devices as the data collecting nodes that are able to send the data to remote application servers or users to facilitate the visualization and monitoring of related tasks. Millions of IoT devices can be integrated with the presented architecture to improve their scalability.
- Our architecture is able to cope up with standard IEEE 802.1X authentication, which works on top of the IEEE 802.11u standard. This ensures a secure and more effective authentication process.
3. Background
3.1. Internet of Thing (IoT)
3.2. Blockchain
3.3. Dew Computing
3.4. Hotspot
3.4.1. Tethering
3.4.2. Hotspot Varieties
3.4.3. Hotspot 2.0
- STA passive scan: The Hotspot 2.0 access point sends a Beacon frame to the STA module that comprises of network type, Hotspot 2.0 indication, and related network information. Upon receipt of the Beacon frame, the STA module checks the Hotspot 2.0 indication into it. The STA module can learn about basic service set (BSS) load prior to establishing a connection with the access point. If all works correctly, the STA module performs the parsing of the roaming consortium field in the Beacon frame to get details about the OI of the WLAN service provider.
- STA active scan: During this scan, the STA module sends a Probe Request frame to the access point along with the network type information. Upon receipt of this frame, the access point matches with the network type of the frame with its own network type. If the network type is matched, it sends the Probe Response frame to the STA module with the necessary BSS load, the internet connectivity flag, and other network details. After receipt of the Probe Response frame at the STA module, the STA module checks the hotspot indication. If everything works correctly, then the STA module assumes that the access point has the Hotspot 2.0 facility and the other activities are performed as in active scan procedure.
- The STA gathers roaming WLAN information: The generic advertisement service (GAS) is a mechanism that allows an STA module to exchange requests and response packets with the WLAN side. Firstly, the STA sends a GAS Initial Request to the access point along with supported authentication types, Hotspot 2.0 operators, and related details. Upon the receipt of such a packet, the access point responds with GAS Initial Response packet that contains the ANQP-structured contents such as, the roaming consortium list, domain name, venue name, venue info, operator friendly name, IP address type availability, connection capacity, network authentication type information, access network type field, internet available field, BSS load information, Hotspot 2.0 indication, operating class indication, network access identifier (NAI) realm, 3GPP cellular public land mobile network (PLMN), and the homogeneous extended service set (HESSID).
- The STA association with the access point: Upon the detection of a target WLAN, the STA module sends an association request to the access point with the NAI realm, network type, authentication types, and Hotspot 2.0 indication. If all works correctly, the access point responds back with an association response frame to the STA module where the advanced encryption standard (AES)-aware 802.1X authentication procedure is embedded.
- STA authentication: An STA module sends an 802.1X authentication request to the access point, which then forwards it to the 802.1X authentication server (AAA) via an 802.1X relay (access controller) along with the NAI reports. A home authentication server (AAA) then communicates with the remote AAA server for the requisite authentication approval. If all works correctly, remote AAA server then grants access of the WLAN to the STA module.
3.4.4. Hotspot Gateway
3.4.5. Hotspot Security Issues
- It does not solve interference problems.
- It faces an installed base hurdle because old access point replacement is a tedious task.
- Possible eavesdropping may be induced in terms of a man-in-the-middle attack.
- WLAN encryption is performed at the surface or interface level, later the message travels via the underlying network stack in an unencrypted manner to the remote service provider (ISP), thus causing risk.
- Public hotspots are prone to collect the users’ metadata and related content, which require more secure access methods such as HTTPS and SSH.
- Despite authenticating the users, users may be able to peek into the network traffic by using a packet sniffer mechanism.
- Some business vendors provide a download option for Wi-Fi protected access (WPA) which may cause conflict with enterprise configurations which match with their own WLAN specifications.
4. Distributed Hotspot Network Architecture
4.1. IoT Device Layer
4.2. Distributed and Decentralized Hotspot Network Layer
4.2.1. Hotspot Gateway
4.2.2. 802.1X Authentication
4.2.3. Dew Server Computation
4.2.4. Dew Server as Hotspots
4.2.5. Types of Hotspots
- Full hotspot: Dew servers, which can be configured as full hotspots, can be eligible to perform coverage facility to the IoT devices that are in their vicinity and also participate in all types of potential crypto reward scenarios inside the network. The Proof-of-Coverage (PoC) can be seen as a dominant algorithm in such an aspect as this. However, it should be maintained that such full hotspots should have prior approval from the underlying network authority, with high-standard and failure-proof subjunctives. We apprehend that the higher specification of servers with a complex processor design and memory capacity are mandatory for such type of hotspots.
- Light-weight hotspot: Such hotspots can be configured to replace full hotspots when moderate-load and moderate computations are needed, seamlessly. Dew servers have great potential to become converted into light-weight hotspots. They can perform regular hotspot coverage and perform header-wise synchronization with the associated blockchain. In this context, when overloading is perceived on the light-weight hotspots, they can transfer some consensus work to the full hotspots which are then expected to act as the validator of the light-weight hotspot. The use of light-weight hotspots can simplify the network structure and it enables the network ecosystem to grow rapidly.
- Data-only hotspot: This type of hotspot can only perform network data transfer. The transfer of data that are related to crypto awards may be earned by the use of such hotspots. We do not expect that the data-only hotspot would participate in the PoC-aware reward. Thus, a permissionless approach may be incorporated in the blockchain. users start earning crypto tokens as and when they are allowed to add blocks to the blockchain.
4.2.6. Proof of Coverage
- The radio frequency has a limited range for propagation.
- The radio frequency signal that is received at a terminal can be used to measure the signal strength by applying the proportional squared distance law.
- Radio frequencies have a minimum latency because they propagate at the speed of light.
- Interrogator: Usually, such nodes are full hotspot nodes or other designated validators that create the PoC challenge and issue it to the condemned node. It challenges the PoC for a convicted target node.
- Convicted: it is a hotspot node that is the target of the PoC challenge, and it is expected to transmit the challenge packets so that the nearby hotspots can observe its activity.
- Witness: Such hotspots are located in the immediate (geographic) vicinity of the convicted node and also report to the querying system, the status of the challenge packets that is sent by the convicted node (High Performance Remote Procedure Call-gRPC). Such a witness is directly connected to all the light-weight hotspot nodes. Such lightweight nodes, that are PoC challenge witnesses, can use validators to which they are connected so that the entire query validator search process can be managed using the hash of the PoC packet. This routing information is later used by the light-weight hotspot to deliver the witness report directly to the query sender. Once the query sender receives both the convicted receipts and witness reports after a certain time, it transmits them to the blockchain and the PoC challenge is complete.
4.2.7. PoC Challenge Creation and Target Selection
4.2.8. Crypto Mining and Rewards
4.2.9. Network Consensus Protocol Goals
4.3. Internetwork Backhaul
4.4. Network Servers/Operators
4.5. Application Servers/Service Providers/Remote Users
5. Discussion
5.1. Threat Model
5.2. Key Challenges
- The architecture is a conceptual model; thus, it needs to be implemented. The implementation of this architecture would require dedicated dew servers and hotspot coverage antennae such as Wi-Fi or LoraWAN. The selection of the Wi-Fi module could be judiciously performed so that long range coverage can be facilitated with a higher bandwidth. However, such antennae should consume a low level of power for providing better sustainability. LoraWAN could be a great choice in this regard, however, the cost of antenna module could be high for very long-range coverage.
- IoT devices should have Wi-Fi or LoraWAN connectivity to communicate to the nearby hotspots. Thus, a serious consideration should be made so that the cost of the IoT device does not go beyond a certain limit and so that the battery consumption can be minimized.
- Hotspots need to be configured as miner nodes that should run on top of dew servers. Synchronization algorithms should be devised for making the internet independency more reliable.
- Several LoraWAN platforms including TTN, Helium, LORIOT, ResIOT, SenRa, and ChirpStack can be considered while one is considering the internetwork backhaul. For Wi-Fi hotspots, a standard cellular backhaul may be used.
- The type of blockchain should be devised. A hybrid approach can be beneficial in this aspect. An owner of a hotspot miner device can place it in their locality, to act as one of three types of hotspots, namely, full, light-weight, or data-only. The design specifications of each type of hotspot are different as their tasks are different. Complex, moderate, and simple hotspot hardware designs should be selected prior to deploying them in the real field of application.
- Concise decisions should be made about the use of a PoC challenging aspect in this architecture. The PoC challenge rate, epoch size, and block time are important parameter that must be resolved a-priori.
- One should consider the wallet type while aiming to connect their hotspot miner node with this network architecture.
- A network consensus algorithm can be revisited to improve the reliability of the hotspot network. Target crypto token production per unit of time (e.g., month, quarter, half-yearly, or yearly) need to be accorded.
- It is important to select a cryptocurrency that will be used in this architecture for rewarding the hotspot owners. It can be selected from existing standard cryptocurrencies or can be devised indigenously for a specific hotspot-distributed network architecture.
- The structure of the block should be designed optimally. The number of transactions per block should be decided before using the blocks in reality. A decision should be made for fixing the transaction fees. The oracles should be carefully decided to specify the data credit conversion rate. The price of the selected crypto oracles must be aligned with the other blockchain networks.
- The interoperability issues should be tackled so that this architecture can talk with other blockchains.
- A trustless packet purchasing features should be formulated in order to allow higher coverage for the hotspot network. State channels and an organizationally unique identifier (OUI) should be implemented with proper care.
- A reward scaling approach must be put in place for each epoch. In this aspect, it becomes important to specify who gets what, i.e., a hotspot shall earn as specific amount, as a reward.
- The selection of a higher-grade Byzantine fault-tolerant protocol becomes inevitable when a blockchain is used. An asynchronous atomic broadcast protocol can be used with a consensus group which has known nodes. The threshold encryption technique may be deployed to improve the async behavior.
- A procedure should be designed to elect a consensus group. It can be performed epoch-wise or in a time/duration manner. The number of members of each consensus group should be judiciously decided.
- The overall governance of the hotspot network must be catered with regular voting and community guidelines.
- The scalability aspects should be investigated for the mass IoT-based dew deployments for the provisioning of the distributed hotspot network.
- The diverse network connectivity of such architecture must be well designed for mitigating a significant amount of channel stabilization. Thus, this issue must be further investigated.
- Dewlet-aware rental services should be invoked with fairness practices. Innovative methods should be investigated to mitigate this issue.
- A detailed threat model analysis is not available for this architecture. Without such a model analysis, it is difficult to state the viability of the proposed system.
5.3. Future Scope
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Ogonji, M.M.; Okeyo, G.; Wafula, J.M. A survey on privacy and security of Internet of Things. Comput. Sci. Rev. 2020, 38, 100312. [Google Scholar] [CrossRef]
- Lombardi, M.; Pascale, F.; Santaniello, D. Internet of things: A general overview between architectures, protocols and applications. Information 2021, 12, 87. [Google Scholar] [CrossRef]
- Raj, M.; Gupta, S.; Chamola, V.; Elhence, A.; Garg, T.; Atiquzzaman, M.; Niyato, D. A survey on the role of Internet of Things for adopting and promoting Agriculture 4.0. J. Netw. Comput. Appl. 2021, 187, 103107. [Google Scholar] [CrossRef]
- Lee, E.; Seo, Y.D.; Oh, S.R.; Kim, Y.G. A Survey on Standards for Interoperability and Security in the Internet of Things. IEEE Commun. Surv. Tutor. 2021, 23, 1020–1047. [Google Scholar] [CrossRef]
- Ratta, P.; Kaur, A.; Sharma, S.; Shabaz, M.; Dhiman, G. Application of blockchain and internet of things in healthcare and medical sector: Applications, challenges, and future perspectives. J. Food Qual. 2021, 2021, 7608296. [Google Scholar] [CrossRef]
- Misra, N.N.; Dixit, Y.; Al-Mallahi, A.; Bhullar, M.S.; Upadhyay, R.; Martynenko, A. IoT, big data and artificial intelligence in agriculture and food industry. IEEE Internet Things J. 2020, 9, 6305–6324. [Google Scholar] [CrossRef]
- Laghari, A.A.; Wu, K.; Laghari, R.A.; Ali, M.; Khan, A.A. A review and state of art of Internet of Things (IoT). Arch. Comput. Methods Eng. 2021, 29, 1395–1413. [Google Scholar] [CrossRef]
- Sobin, C.C. A survey on architecture, protocols and challenges in IoT. Wirel. Pers. Commun. 2020, 112, 1383–1429. [Google Scholar] [CrossRef]
- Hossein Motlagh, N.; Mohammadrezaei, M.; Hunt, J.; Zakeri, B. Internet of Things (IoT) and the energy sector. Energies 2020, 13, 494. [Google Scholar] [CrossRef]
- Khalaf, O.I.; Romero, C.A.T.; Hassan, S.; Iqbal, M.T. Mitigating hotspot issues in heterogeneous wireless sensor networks. J. Sens. 2022, 2022, 7909472. [Google Scholar] [CrossRef]
- Dolan, E.; Widayanti, R. Implementation Of Authentication Systems On Hotspot Network Users To Improve Computer Network Security. Int. J. Cyber IT Serv. Manag. 2022, 2, 88–94. [Google Scholar] [CrossRef]
- Jiang, Y.; Yang, F.; Yu, B.; Zhou, D.; Zeng, X. Efficient layout hotspot detection via binarized residual neural network ensemble. IEEE Trans. Comput. -Aided Des. Integr. Circuits Syst. 2020, 40, 1476–1488. [Google Scholar] [CrossRef]
- Swedha, S.; Gopi, E.S. LSTM network for hotspot prediction in traffic density of cellular network. In Machine Learning, Deep Learning and Computational Intelligence for Wireless Communication; Springer: Singapore, 2021; pp. 35–47. [Google Scholar]
- Gushev, M. Dew computing architecture for cyber-physical systems and IoT. Internet Things 2020, 11, 100186. [Google Scholar] [CrossRef]
- Singh, P.; Kaur, A.; Aujla, G.S.; Batth, R.S.; Kanhere, S. Daas: Dew computing as a service for intelligent intrusion detection in edge-of-things ecosystem. IEEE Internet Things J. 2020, 8, 12569–12577. [Google Scholar] [CrossRef]
- Podder, T.; Bhattacharya, D.; Majumdar, A. Dew Computing-Inspired Mental Health Monitoring System Framework Powered by a Lightweight CNN. In Disruptive Technologies for Big Data and Cloud Applications; Springer: Singapore, 2022; pp. 309–319. [Google Scholar]
- Olabisi, D.; Abubakar, S.K.; Abdullahi, A.T. Demystifying Dew Computing: Concept, Architecture and Research Opportunities. Int. J. Comput. Trends Technol. 2022, 70, 39–43. [Google Scholar] [CrossRef]
- Wang, Y. A blockchain system with lightweight full node based on dew computing. Internet Things 2020, 11, 100184. [Google Scholar] [CrossRef]
- Manocha, A.; Bhatia, M.; Kumar, G. Dew computing-inspired health-meteorological factor analysis for early prediction of bronchial asthma. J. Netw. Comput. Appl. 2021, 179, 102995. [Google Scholar] [CrossRef]
- Hirsch, M.; Mateos, C.; Rodriguez, J.M.; Zunino, A. DewSim: A trace-driven toolkit for simulating mobile device clusters in Dew computing environments. Softw. Pract. Exp. 2020, 50, 688–718. [Google Scholar] [CrossRef]
- Moussa, M.M.; Alazzawi, L. Cyber attacks detection based on deep learning for cloud-dew computing in automotive iot applications. In Proceedings of the 2020 IEEE International Conference on Smart Cloud (SmartCloud), Washington, DC, USA, 6–8 November 2020; pp. 55–61. [Google Scholar]
- Draz, U.; Ali, T.; Yasin, S.; Waqas, U.; Rafiq, U. EADSA: Energy-aware distributed sink algorithm for hotspot problem in wireless sensor and actor networks. In Proceedings of the 2019 International Conference on Engineering and Emerging Technologies (ICEET), Lahore, Pakistan, 21–22 February 2019; pp. 1–6. [Google Scholar]
- Ye, A.; Li, Q.; Zhang, Q.; Cheng, B. Detection of spoofing attacks in WLAN-based positioning systems using Wi-Fi hotspot tags. IEEE Access 2020, 8, 39768–39780. [Google Scholar] [CrossRef]
- Wang, X.; Lin, F.; Wu, Y. A novel positioning system of potential Wi-Fi hotspots for software defined Wi-Fi network planning. In Proceedings of the 2019 16th IEEE Annual Consumer Communications & Networking Conference (CCNC), Las Vegas, NV, USA, 11–14 January 2019; pp. 1–6. [Google Scholar]
- Noura, H.; Hatoum, T.; Salman, O.; Yaacoub, J.P.; Chehab, A. LoRaWAN security survey: Issues, threats and possible mitigation techniques. Internet Things 2020, 12, 100303. [Google Scholar] [CrossRef]
- Jouhari, M.; Amhoud, E.M.; Saeed, N.; Alouini, M.S. A Survey on Scalable LoRaWAN for Massive IoT: Recent Advances, Potentials, and Challenges. arXiv 2022, arXiv:2202.11082. [Google Scholar]
- Zhou, Q.; Huang, H.; Zheng, Z.; Bian, J. Solutions to scalability of blockchain: A survey. IEEE Access 2020, 8, 16440–16455. [Google Scholar] [CrossRef]
- Syed, T.A.; Alzahrani, A.; Jan, S.; Siddiqui, M.S.; Nadeem, A.; Alghamdi, T. A comparative analysis of blockchain architecture and its applications: Problems and recommendations. IEEE Access 2019, 7, 176838–176869. [Google Scholar] [CrossRef]
- Zheng, W.; Zheng, Z.; Chen, X.; Dai, K.; Li, P.; Chen, R. NutBaaS: A blockchain-as-a-service platform. IEEE Access 2019, 7, 134422–134433. [Google Scholar] [CrossRef]
- Wang, Q.; Zhu, X.; Ni, Y.; Gu, L.; Zhu, H. Blockchain for the IoT and industrial IoT: A review. Internet Things 2020, 10, 100081. [Google Scholar] [CrossRef]
- Singh, S.; Hosen, A.S.; Yoon, B. Blockchain security attacks, challenges, and solutions for the future distributed iot network. IEEE Access 2021, 9, 13938–13959. [Google Scholar] [CrossRef]
- Zha, D.S.; Feng, T.T.; Gong, X.L.; Liu, S.Y. When energy meets blockchain: A systematic exposition of policies, research hotspots, applications, and prospects. Int. J. Energy Res. 2022, 46, 2330–2360. [Google Scholar] [CrossRef]
- Zhao, X.; Lei, Z.; Zhang, G.; Zhang, Y.; Xing, C. September. Blockchain and distributed system. In International Conference on Web Information Systems and Applications; Springer: Cham, Switzerland, 2020; pp. 629–641. [Google Scholar]
- Messié, V.; Fromentoux, G.; Labidurie, N.; Radier, B.; Vaton, S.; Amigo, I. BALAdIN: Truthfulness in collaborative access networks with distributed ledgers. Ann. Telecommun. 2022, 77, 47–59. [Google Scholar] [CrossRef]
- Lopez, D.; Yazdizadeh, A.; Farooq, B.; Patterson, Z. Distributed Privacy-Aware Choice Modelling using Federated Learning over Blockchain. In Proceedings of the International Choice Modelling Conference, Kobe, Japan, 19–21 August 2019. [Google Scholar]
- Janiesch, C.; Fischer, M.; Imgrund, F.; Hofmann, A.; Winkelmann, A. An Architecture Using Payment Channel Networks for Blockchain-based Wi-Fi Sharing: An Architecture for Blockchain-based Wi-Fi Sharing. ACM Trans. Manag. Inf. Syst. 2022. [Google Scholar] [CrossRef]
- Yang, Y.; Liu, Z.; Liu, Z.; Chan, K.Y.; Guan, X. Joint Optimization of Edge Computing Resource Pricing and Wireless Caching for Blockchain-Driven Networks. IEEE Trans. Veh. Technol. 2022, 71, 6661–6670. [Google Scholar] [CrossRef]
- Zhao, Z.; Guo, J.; Luo, X.; Xue, J.; Lai, C.S.; Xu, Z.; Lai, L.L. Energy transaction for multi-microgrids and internal microgrid based on blockchain. IEEE Access 2020, 8, 144362–144372. [Google Scholar] [CrossRef]
- Kim, S.K.S. Apply Blockchain to Overcome Wi-Fi Vulnerabilities. J. Multimed. Inf. Syst. 2019, 6, 139–146. [Google Scholar] [CrossRef]
- Ivanov, N.; Lou, J.; Yan, Q. Smart Wi-Fi: Universal and secure smart contract-enabled Wi-Fi hotspot. In International Conference on Security and Privacy in Communication Systems; Springer: Cham, Switzerland, 2020; pp. 425–445. [Google Scholar]
- Pustišek, M.; Dolenc, D.; Kos, A. LDAF: Low-bandwidth distributed applications framework in a use case of blockchain-enabled IoT devices. Sensors 2019, 19, 2337. [Google Scholar] [CrossRef]
- Ma, S.; Li, H.; Yang, W.; Li, J.; Nepal, S.; Bertino, E. Certified Copy? Understanding Security Risks of Wi-Fi Hotspot based Android Data Clone Services. In Proceedings of the Annual Computer Security Applications Conference, Austin, TX, USA, 7–11 December 2020; pp. 320–331. [Google Scholar]
- Casado-Vara, R.; Novais, P.; Gil, A.B.; Prieto, J.; Corchado, J.M. Distributed continuous-time fault estimation control for multiple devices in IoT networks. IEEE Access 2019, 7, 11972–11984. [Google Scholar] [CrossRef]
- Babun, L.; Denney, K.; Celik, Z.B.; McDaniel, P.; Uluagac, A.S. A survey on IoT platforms: Communication, security, and privacy perspectives. Comput. Netw. 2021, 192, 108040. [Google Scholar] [CrossRef]
- Boursianis, A.D.; Papadopoulou, M.S.; Diamantoulakis, P.; Liopa-Tsakalidi, A.; Barouchas, P.; Salahas, G.; Karagiannidis, G.; Wan, S.; Goudos, S.K. Internet of things (IoT) and agricultural unmanned aerial vehicles (UAVs) in smart farming: A comprehensive review. Internet Things 2022, 18, 100187. [Google Scholar] [CrossRef]
- Lounis, K.; Zulkernine, M. Attacks and defenses in short-range wireless technologies for IoT. IEEE Access 2020, 8, 88892–88932. [Google Scholar] [CrossRef]
- Balcerzak, A.P.; Nica, E.; Rogalska, E.; Poliak, M.; Klieštik, T.; Sabie, O.M. Blockchain Technology and Smart Contracts in Decentralized Governance Systems. Adm. Sci. 2022, 12, 96. [Google Scholar] [CrossRef]
- Alshehri, F.; Muhammad, G. A comprehensive survey of the Internet of Things (IoT) and AI-based smart healthcare. IEEE Access 2020, 9, 3660–3678. [Google Scholar] [CrossRef]
- Husnoo, M.A.; Anwar, A.; Chakrabortty, R.K.; Doss, R.; Ryan, M.J. Differential privacy for IoT-enabled critical infrastructure: A comprehensive survey. IEEE Access 2021, 9, 153276–153304. [Google Scholar] [CrossRef]
- Mohanta, B.K.; Jena, D.; Satapathy, U.; Patnaik, S. Survey on IoT security: Challenges and solution using machine learning, artificial intelligence and blockchain technology. Internet Things 2020, 11, 100227. [Google Scholar] [CrossRef]
- Nižetić, S.; Šolić, P.; González-de, D.L.D.I.; Patrono, L. Internet of Things (IoT): Opportunities, issues and challenges towards a smart and sustainable future. J. Clean. Prod. 2020, 274, 122877. [Google Scholar] [CrossRef] [PubMed]
- Chettri, L.; Bera, R. A comprehensive survey on Internet of Things (IoT) toward 5G wireless systems. IEEE Internet Things J. 2019, 7, 16–32. [Google Scholar] [CrossRef]
- Hassan, W.H. Current research on Internet of Things (IoT) security: A survey. Comput. Netw. 2019, 148, 283–294. [Google Scholar]
- Wu, M.; Wang, K.; Cai, X.; Guo, S.; Guo, M.; Rong, C. A comprehensive survey of blockchain: From theory to IoT applications and beyond. IEEE Internet Things J. 2019, 6, 8114–8154. [Google Scholar] [CrossRef]
- Pavithran, D.; Shaalan, K.; Al-Karaki, J.N.; Gawanmeh, A. Towards building a blockchain framework for IoT. Clust. Comput. 2020, 23, 2089–2103. [Google Scholar] [CrossRef]
- Gill, S.S.; Tuli, S.; Xu, M.; Singh, I.; Singh, K.V.; Lindsay, D.; Tuli, S.; Smirnova, D.; Singh, M.; Jain, U.; et al. Transformative effects of IoT, Blockchain and Artificial Intelligence on cloud computing: Evolution, vision, trends and open challenges. Internet Things 2019, 8, 100118. [Google Scholar] [CrossRef]
- Dedeoglu, V.; Jurdak, R.; Dorri, A.; Lunardi, R.C.; Michelin, R.A.; Zorzo, A.F.; Kanhere, S.S. Blockchain technologies for iot. In Advanced Applications of Blockchain Technology; Springer: Singapore, 2020; pp. 55–89. [Google Scholar]
- Shahbazi, Z.; Byun, Y.C. Integration of Blockchain, IoT and machine learning for multistage quality control and enhancing security in smart manufacturing. Sensors 2021, 21, 1467. [Google Scholar] [CrossRef]
- Rane, S.B.; Thakker, S.V. Green procurement process model based on blockchain–IoT integrated architecture for a sustainable business. Manag. Environ. Qual. Int. J. 2020, 31, 741–763. [Google Scholar] [CrossRef]
- Si, H.; Sun, C.; Li, Y.; Qiao, H.; Shi, L. IoT information sharing security mechanism based on blockchain technology. Future Gener. Comput. Syst. 2019, 101, 1028–1040. [Google Scholar] [CrossRef]
- Tseng, L.; Yao, X.; Otoum, S.; Aloqaily, M.; Jararweh, Y. Blockchain-based database in an IoT environment: Challenges, opportunities, and analysis. Clust. Comput. 2020, 23, 2151–2165. [Google Scholar] [CrossRef]
- Alladi, T.; Chamola, V.; Parizi, R.M.; Choo, K.K.R. Blockchain applications for industry 4.0 and industrial IoT: A review. IEEE Access 2019, 7, 176935–176951. [Google Scholar] [CrossRef]
- Sun, S.; Du, R.; Chen, S.; Li, W. Blockchain-based IoT access control system: Towards security, lightweight, and cross-domain. IEEE Access 2021, 9, 36868–36878. [Google Scholar] [CrossRef]
- Sharma, P.K.; Kumar, N.; Park, J.H. Blockchain technology toward green IoT: Opportunities and challenges. IEEE Netw. 2020, 34, 263–269. [Google Scholar] [CrossRef]
- Hirsch, M.; Mateos, C.; Zunino, A.; Majchrzak, T.A.; Grønli, T.M.; Kaindl, H. A task execution scheme for dew computing with state-of-the-art smartphones. Electronics 2021, 10, 2006. [Google Scholar] [CrossRef]
- Ahammad, I.; Khan, A.R.; Salehin, Z.U. A Review on Cloud, Fog, Roof, and Dew Computing: IoT Perspective. Int. J. Cloud Appl. Comput. (IJCAC) 2021, 11, 14–41. [Google Scholar] [CrossRef]
- Gusev, M. Serverless and Deviceless Dew Computing: Founding an Infrastructureless Computing. In Proceedings of the 2021 IEEE 45th Annual Computers, Software, and Applications Conference (COMPSAC), Madrid, Spain, 12–16 July 2021; pp. 1814–1818. [Google Scholar]
- Gusev, M. What makes Dew computing more than Edge computing for Internet of Things. In Proceedings of the 2021 IEEE 45th Annual Computers, Software, and Applications Conference (COMPSAC), Madrid, Spain, 12–16 July 2021; pp. 1795–1800. [Google Scholar]
- Javadzadeh, G.; Rahmani, A.M.; Kamarposhti, M.S. Mathematical model for the scheduling of real-time applications in IoT using Dew computing. J. Supercomput. 2022, 78, 7464–7488. [Google Scholar] [CrossRef]
- Sverko, M.; Tankovic, N.; Etinger, D. Dew Computing in Industrial Automation: Applying Machine Learning for Process Control. In Proceedings of the 2021 IEEE 45th Annual Computers, Software, and Applications Conference (COMPSAC), Madrid, Spain, 12–16 July 2021; pp. 1789–1794. [Google Scholar]
- Braeken, A. Authenticated key agreement protocols for dew-assisted IoT systems. J. Supercomput. 2022, 78, 12093–12113. [Google Scholar] [CrossRef]
- Mukherjee, A.; De, D.; Dey, N. Dewdrone: Dew computing for Internet of Drone Things. IEEE Consum. Electron. Mag. 2021. [Google Scholar] [CrossRef]
- Islam, A.; Al Amin, A.; Shin, S.Y. FBI: A federated learning-based blockchain-embedded data accumulation scheme using drones for Internet of Things. IEEE Wirel. Commun. Lett. 2022, 11, 972–976. [Google Scholar] [CrossRef]
- Gusev, M. AI cardiologist at the edge: A use case of a dew computing heart monitoring solution. In Artificial Intelligence and Machine Learning for EDGE Computing; Academic Press: Cambridge, UK, 2022; pp. 469–477. [Google Scholar]
- Rana, S.; Obaidat, M.S.; Mishra, D.; Mishra, A.; Rao, Y.S. Efficient design of an authenticated key agreement protocol for dew-assisted IoT systems. J. Supercomput. 2022, 78, 3696–3714. [Google Scholar] [CrossRef]
- Medhi, K.; Ahmed, N.; Hussain, M.I. Dew-based offline computing architecture for healthcare IoT. ICT Express 2022, 8, 371–378. [Google Scholar] [CrossRef]
- Guberović, E.; Lipić, T.; Čavrak, I. Dew Intelligence: Federated learning perspective. In Proceedings of the 2021 IEEE 45th Annual Computers, Software, and Applications Conference (COMPSAC), Madrid, Spain, 12–16 July 2021; pp. 1819–1824. [Google Scholar]
- Aishwarya, M.R.; Mathivanan, G. AI Strategy for Stake Cloud Computing and Edge Computing: A State of the art survey. In Proceedings of the 2021 5th International Conference on Electronics, Communication and Aerospace Technology (ICECA), Coimbatore, India, 2–4 December 2021; pp. 920–927. [Google Scholar]
- Aburukba, R.; Al-Ali, A.R.; Riaz, A.H.; Al Nabulsi, A.; Khan, D.; Khan, S.; Amer, M. Fog Computing Approach for Shared Mobility in Smart Cities. Energies 2021, 14, 8174. [Google Scholar] [CrossRef]
- Escobar-Diaz, F.; Buitrago, C.; Quiñones, L.; Grajales, F.; Mejia, T. Evaluation of particulate matter microsensors to build the low-cost sensors collaborative network of Bogotá. In Proceedings of the 2021 Congreso Colombiano y Conferencia Internacional de Calidad de Aire y Salud Pública (CASAP), Bogota, Colombia, 3–5 November 2021; pp. 1–5. [Google Scholar]
- Costa, B.; Bachiega, J., Jr.; de Carvalho, L.R.; Araujo, A.P. Orchestration in fog computing: A comprehensive survey. ACM Comput. Surv. (CSUR) 2022, 55, 1–34. [Google Scholar] [CrossRef]
- Dong, W.; Lv, J.; Chen, G.; Wang, Y.; Li, H.; Gao, Y.; Bharadia, D. TinyNet: A lightweight, modular, and unified network architecture for the internet of things. In Proceedings of the 20th Annual International Conference on Mobile Systems, Applications and Services, Portland, OR, USA, 27 June–1 July 2022; pp. 248–260. [Google Scholar]
- Veloso, A.F.D.S.; Júnior, J.V.R.; Rabelo, R.D.A.L.; Silveira, J.D.F. HyDSMaaS: A Hybrid Communication Infrastructure with LoRaWAN and LoraMesh for the Demand Side Management as a Service. Future Internet 2021, 13, 271. [Google Scholar] [CrossRef]
- Schütz, M. RF Harvesting at 2.4 GHz for Scattering between Battery-less Transponder and Mobile Telephones. In Proceedings of the 2021 IEEE International Conference on RFID Technology and Applications (RFID-TA), Delhi, India, 6–8 October 2021; pp. 93–96. [Google Scholar]
- Mishra, V.K.; Swami, B.D.; Kanagarathinam, M.R.; Thorat, P.B.; Das, D. NextGen-MHS: A Novel Architecture for Tethering of Aggregated Licensed and Unlicensed Spectrums. In Proceedings of the 2019 IEEE Wireless Communications and Networking Conference (WCNC), Marrakesh, Morocco, 15–18 April 2019; pp. 1–6. [Google Scholar]
- Agyemang, J.O.; Kponyo, J.J.; Klogo, G.S.; Boateng, J.O. Lightweight rogue access point detection algorithm for Wi-Fi-enabled Internet of Things (IoT) devices. Internet Things 2020, 11, 100200. [Google Scholar] [CrossRef]
- Xu, W.; Zhou, H.; Bi, Y.; Cheng, N.; Shen, X.; Thanayankizil, L.; Bai, F. Exploiting hotspot-2.0 for traffic offloading in mobile networks. IEEE Netw. 2018, 32, 131–137. [Google Scholar] [CrossRef]
- Nojima, D.; Yamada, A. Technologies for Interworking Between Cellular and WLAN Systems. IEICE Commun. Soc. Glob. Newsl. 2019, 43, 3. [Google Scholar]
- Bednarczyk, M. IEEE 802.11 ax: Giant leap in WLAN evolution. In Proceedings of the XII Conference on Reconnaissance and Electronic Warfare Systems, Oltarzew, Poland, 19–21 November 2018; Volume 11055, pp. 416–422. [Google Scholar]
- Chatzisofroniou, G.; Kotzanikolaou, P. Exploiting WiFi usability features for association attacks in IEEE 802.11: Attack analysis and mitigation controls. J. Comput. Secur. 2022, 30, 357–380. [Google Scholar] [CrossRef]
- Huawei Hotspot 2.0. Available online: https://support.huawei.com/enterprise/en/doc/EDOC1100096325/2010a98b/understanding-hotspot-20 (accessed on 16 July 2022).
- Zhang, Z.; Wang, Y.; Yang, K. Strong Authentication without Temper-Resistant Hardware and Application to Federated Identities. In Proceedings of the NDSS 2020, San Diego, CA, USA, 23–26 February 2020. [Google Scholar]
- Li, Z.; Wang, D.; Morais, E. Quantum-safe round-optimal password authentication for mobile devices. IEEE Trans. Dependable Secur. Comput. 2020, 19, 1885–1899. [Google Scholar] [CrossRef]
- Paolini, A.; Scardaci, D.; Liampotis, N.; Spinoso, V.; Grenier, B.; Chen, Y. Authentication, authorization, and accounting. In Towards Interoperable Research Infrastructures for Environmental and Earth Sciences; Springer: Cham, Switzerland, 2020; pp. 247–271. [Google Scholar]
- Helium Network. Available online: https://docs.helium.com/ (accessed on 15 July 2022).
- Helium Mining. Available online: https://www.okdo.com/blog/the-ultimate-guide-to-lora-helium-miners-and-crypto-mining/ (accessed on 16 July 2022).
- Helium Network Design. Available online: https://create.arduino.cc/projecthub/akarsh98/what-is-helium-network-hnt-mining-hotspots-and-crypto-7a148e (accessed on 16 July 2022).
- Helium Hotspot Mining. Available online: https://create.arduino.cc/projecthub/akarsh98/tutorial-helium-light-hotspot-with-dragino-lps8-dlos8-miner-b7a39e (accessed on 16 July 2022).
- Proof of Coverage. Available online: https://docs.helium.com/blockchain/proof-of-coverage (accessed on 15 July 2022).
- Helium Network White Paper. Available online: http://whitepaper.helium.com/ (accessed on 14 July 2022).
- Wang, D.; Wang, P. Two birds with one stone: Two-factor authentication with security beyond conventional bound. IEEE Trans. Dependable Secur. Comput. 2016, 15, 708–722. [Google Scholar] [CrossRef]
- NTP Amplified Reflection DDOS Attack Scanning Tutorial, Amplified List Scanning Tutorial. Available online: http://www.aeys.org/thread-3520-1-1.html/ (accessed on 5 August 2022).
- Granata, D.; Rak, M.; Salzillo, G.; Barbato, U. Security in IoT Pairing & Authentication protocols, a Threat Model, a Case Study Analysis. In Proceedings of the ITASEC 2021, Virtual, 7–9 April 2021; pp. 207–218. [Google Scholar]
- The STRIDE Threat Model. Available online: https://docs.microsoft.com/en-us/previous-versions/commerce-server/ee823878(v=cs.20)?redirectedfrom=MSDN (accessed on 25 August 2022).
Paper | Blockchain | IoT | Hotspot | Wi-Fi | LoraWAN | Dew Computing | Key Contributions | Limitations |
---|---|---|---|---|---|---|---|---|
Zha et al. [32] | Yes | No | Yes | Partial | Partial | No | Blockchain aware energy, review, policy recommendations, applications; | Lacks hotspot network design approach |
Zhao et al. [33] | Yes | Partial | No | No | No | No | Blockchain distributed network design aspects, traceable, tamper-proof design; | Hotspot discussion is minimal |
Messié et al. [34] | Yes | No | Yes | Partial | No | No | BALAdIN framework, multi-actor access network; | No clear direction on distributed hotspot |
Lopez et al. [35] | Yes | No | No | Partial | No | No | Choice modeling, federated learning, distributed privacy-aware design; | No analysis about distributed model is made |
Janiesch et al. [36] | Yes | No | Yes | Yes | No | No | Wi-Fi sharing architecture, payment channel networking, evaluation of architecture; | Distributed behavior not analyzed |
Yang et al. [37] | Yes | No | Partial | Partial | No | No | Pricing mode, wireless caching reward, cache quality, cache content dispersion; | Hotspot distributed network not covered |
Zhao et al. [38] | Yes | No | Partial | No | No | No | Energy transaction, multi-microgrid, energy trading; | Hotspot aware design lacks |
Kim et al. [39] | Yes | No | Partial | Yes | No | No | Wi-Fi security model, secure models using smart contracts to safeguard Wi-Fi vulnerability; | Distributed hotspot discussion missing |
Ivanov et al. [40] | Yes | Partial | Yes | Yes | No | No | Smart Wi-Fi architecture, Hansa handshake/service, smart contract, payment, refunds, security analysis; | Hotspot distributed-ness lacks |
Pustišek et al. [41] | Yes | Yes | Yes | Yes | No | No | Low-bandwidth distributed applications framework (LDAF) architecture, distributed model; | Consensus algorithm no specified, no scalability |
Ma et al. [42] | No | No | Yes | Yes | No | No | Security risk analysis, android data cloning, Evaluation; | No blockchain involved |
Our Model | Yes | Yes | Yes | Yes | Yes | Yes | Distributed hotspot architecture design, blockchain aware secure IoT device data transmission, dew computing inclusion, scalable, incentive. | Implementation needed |
ID | Threat | Compromised Asset | Behavior | Threat Agent |
---|---|---|---|---|
T1 | Unauthorized Network Access | LoRaWAN Network | A malicious user aims to associate with malicious IoT device of the user’s LoRawan Network | Malicious User |
T2 | Unauthorized Network Access | Wi-Fi Network | A malicious user aims to associate with malicious IoT device of the user’s Wi-Fi Network | Malicious User |
T3 | Device Hijacking | IoT Device | A malicious user aims to associate with IoT device of the user’s periphery without user’s knowledge or awareness | Malicious User |
T4 | Device Hijacking | Dew Gateway Device | A malicious user aims to associate with dew gateway device of the user’s periphery without user’s knowledge or awareness | Malicious User |
T5 | Data Leakage | Dew Gateway Device | A malicious user aims to access and retrieve data, i.e., device’s location GPS information, user’s credentials about dew gateway device | Malicious User |
T6 | Device Hijacking | Dew Gateway Miner Device | A malicious user aims to associate with dew gateway miner device of the user’s periphery without user’s knowledge or awareness | Malicious User |
T7 | Impersonation | LoRaWAN Network | A malicious user aims to associate with a legitimate IoT device to malicious LoRaWAN Network | Malicious User |
T8 | Impersonation | Wi-Fi Network | A malicious user aims to associate with a legitimate IoT device to malicious Wi-Fi Network | Malicious User |
T9 | Impersonation | IoT Device | A malicious user aims to associate with force with a malicious IoT device by using other user’s credentials | Malicious User |
T10 | Impersonation | Dew Gateway Device | A malicious user aims to associate with force with a malicious dew gateway device by using other user’s credentials | Malicious User |
T11 | Impersonation | Dew Gateway Miner Device | A malicious user aims to associate, with force, with a malicious dew gateway miner device by using other user’s credentials | Malicious User |
T12 | Jamming | LoRaWAN Network | A malicious user aims to disturb the LoRaWAN Network | Malicious User |
T13 | Jamming | Wi-Fi Network | A malicious user aims to disturb the Wi-Fi Network | Malicious User |
T14 | Message Elimination | Dew Gateway Wi-Fi Network | A malicious user aims to delete or eliminate messages of gateway Wi-Fi network | Malicious User |
T15 | Message Elimination | Dew Gateway LoRaWAN Network | A malicious user aims to delete or eliminate messages of gateway Wi-Fi network | Malicious User |
T16 | Exhaustion of Power | IoT Device | A malicious user aims to consume excessive power to resist IoT device work to prevent regular activities | Malicious User |
T17 | Exhaustion of Power | Dew Gateway Device | A malicious user aims to consume excessive power to resist dew gateway device work to prevent regular activities | Malicious User |
T18 | Exhaustion of Power | Dew Gateway Miner Device | A malicious user aims to consume excessive power to resist dew gateway device work to prevent regular activities | Malicious User |
T19 | Impersonation | Dewlet Service | A malicious user aims to replace legitimate dewlet service with a malicious service | Malicious User |
T20 | Impersonation | Dew Server Service | A malicious user aims to replace legitimate dew server-based service with a malicious service | Malicious User |
T21 | Impersonation | Remote User | A malicious user aims to replace legitimate remote user with a malicious user | Malicious User |
T22 | Denial of Service | Remote User | A malicious user aims to make legitimate remote user with a malicious service | Malicious User |
T23 | Denial of Service | Remote Application Server | A malicious user aims to make legitimate remote application server with a malicious service | Malicious User |
T24 | Eavesdropping | LoRaWAN Network | A malicious user aims to retrieve important packets while transmitted over LoRaWAN network | Malicious User |
T25 | Eavesdropping | Wi-Fi Network | A malicious user aims to retrieve important packets while transmitted over LoRaWAN network | Malicious User |
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Ray, P.P.; Skala, K. Internet of Things Aware Secure Dew Computing Architecture for Distributed Hotspot Network: A Conceptual Study. Appl. Sci. 2022, 12, 8963. https://doi.org/10.3390/app12188963
Ray PP, Skala K. Internet of Things Aware Secure Dew Computing Architecture for Distributed Hotspot Network: A Conceptual Study. Applied Sciences. 2022; 12(18):8963. https://doi.org/10.3390/app12188963
Chicago/Turabian StyleRay, Partha Pratim, and Karolj Skala. 2022. "Internet of Things Aware Secure Dew Computing Architecture for Distributed Hotspot Network: A Conceptual Study" Applied Sciences 12, no. 18: 8963. https://doi.org/10.3390/app12188963
APA StyleRay, P. P., & Skala, K. (2022). Internet of Things Aware Secure Dew Computing Architecture for Distributed Hotspot Network: A Conceptual Study. Applied Sciences, 12(18), 8963. https://doi.org/10.3390/app12188963