LBOA: Location-Based Secure Outsourced Aggregation in IoT

Zhang, Junwei; Zong, Yue; Yang, Chao; Miao, Yinbin; Guo, Jingjing

doi:10.1109/access.2019.2908429

Cited by 13 publications

(5 citation statements)

References 40 publications

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“…To decrease the transmission overheads and network bandwidth, Zhang et al 25 developed a Location‐based secure outsourced aggregation (LBOA) method. A cryptographic algorithm is used to share data in location‐critical scenarios securely.…”

Section: Related Workmentioning

confidence: 99%

Internet of Things‐based wireless sensor networks for monitoring access constraint security measures in liable data aggregation

Gunasekaran

2023

Int J Communication

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SummaryA wireless sensor network (WSN) is a network of autonomous, small sensors that can detect, collect, and send data about their surrounding environment. In the Internet of Things (IoT) infrastructure, WSNs are the smart devices that provide the platform with resource input. Security breaches and insider attacks are possible due to the WSN's resource‐constrained design. However, the IoT platform's intelligence may be extended to WSN nodes for managing device and data‐level security. This paper proposes Monitored Access Constraint Security (MACS) to ensure the privacy of data collected via the ubiquitous processing enabled by the Internet of Things. The IoT platform performs frequent checks on the quality of the interactions between the various nodes to ensure that they are functioning properly and that the sensor aggregation instances are accountable. Node liability is considered while adjusting the aggregate level and the continuity. The method guarantees secure information from the environment and the data sources. The quality of the data gathered in the suggested technique is evaluated based on node liability and information extraction feature. Accordingly, security measures are implemented at data gathering and filtering levels and then assessed using a recurrent learning process. Since there are fewer security breaches overall, the rate of aggregation increases. Aggregation loss, delay time, false rate, throughput, and verification time are used to evaluate the performance.

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Section: Related Workmentioning

confidence: 99%

Internet of Things‐based wireless sensor networks for monitoring access constraint security measures in liable data aggregation

Gunasekaran

2023

Int J Communication

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“…Each once in a while, various approaches were proposed to reduce the specific delay of parallel addition [25]. Yet another method of this is determining the "Sum," "C out ," and the terms "generate (g i )" and "propagate" [5,6] by using conservative conditions. For example, if the carryout is, the term "1" is compatible because all the data sources A in and B in are '1' and therefore in in…”

Section: Carry Look-ahead Addermentioning

confidence: 99%

“…There are extremely helpful details and various possible properties in the sensor material. The system will have around 30 billion smart devices [5,6]. However, the microcontroller or processor requires computing capacity to upgrade an essential device [7,8].…”

Section: Introductionmentioning

confidence: 99%

Internet of Things Based Reconfigurable SIMD Processor for High-Speed End Devices in FPGA

2023

Teh. vjesn.

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This research article proposed the reconfigurable Single Instruction Multi Data (SIMD) processor design to speed up the accelerated computing task in IoT operations. Single Instruction Multi Data models leverage the parallel real source to speed up computing accelerated tasks. It proposes the utilization of reconfigurable Kogge Stone-dependent hybrid adder structures, now referred to as KS-CPA, in which reconfiguration occurs during the addition operation. The Least Significant Bits (LSB) are processed using a carry propagate adder, while the Most Significant Bits (MSB) are computed using the Kogge Stone adder. Depending on the data width and deviceaccessible energy resources, the hybrid configuration of the adder offers the 4-bit, 8-bit, and 16-bit addition. The adder form is identified by a shift in the configuration of its Carry Look-ahead and then by a Kogge Stone Adder (KSA). Throughout the activity, the KS-CLA crossbreed configuration is used to attain the fastest speed and low energy usage. The effectiveness, including its proposed hybrid adder, is evaluated by looking at the speed, energy, and area parameters, including a suitable area use during rapid applications in which both less delay and low power adders are required. Considering these, we are structuring an IoT processor that can be reconfigured to gain from SIMD. We have demonstrated that our hybrid adder-enhanced processor saves energy up to 13% and reduces 27% latency. The proposed 16 and 32-bit adders will boost time, power, and Area Delay Product (ADP) by almost 18-24% and 13-19% respectively.

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“…Outsourced Aggregation for IoT) [50] (i) Used in location-critical applications where data aggregation at each location is outsourced and the application demands privacy and confidentiality of location as well as the location strategy. (ii) Public-key encryption is used to protect the privacy of location and order-preserving encryption the confidentiality of user's location strategy.…”

Section: Lboa (Location Based Securementioning

confidence: 99%

A Survey of Data Aggregation Protocols for Energy Conservation in WSN and IoT

Begum

Nandury

2022

Wireless Communications and Mobile Computing

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Conservation of energy has been a major concern for Wireless Sensor Networks (WSNs) and IoT applications. Several strategies were devised, aimed at optimizing energy consumption in these applications, based on: (a) use of low-powered hardware devices, (b) deploying mobile/relay agents for data collection, (c) clustering, and (d) data aggregation. Amongst these, data aggregation is widely acknowledged as an important tool to conserve energy in WSN and IoTs. The paper provides a comprehensive survey of various data aggregation strategies, discusses the efficacy of these strategies in handling issues that are typical to WSN and IoT applications. These issues severely impact the performance metrics such as: energy efficiency, latency, fault-tolerance, network throughput, and network lifetime. Therefore, to optimize the data aggregation approach, an application developer needs to arrive at optimal tradeoffs between these parameters. A major contribution of the paper is to present a holistic review of data aggregation approaches emphasizing the effect of topology, security, mobility, interference, and fault-tolerance in WSN and IoTs. Based on gap areas in literature, we throw open few challenges and present them as “posers”, and put-forth suggestions for further research.

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LBOA: Location-Based Secure Outsourced Aggregation in IoT

Cited by 13 publications

References 40 publications

Internet of Things‐based wireless sensor networks for monitoring access constraint security measures in liable data aggregation

Internet of Things‐based wireless sensor networks for monitoring access constraint security measures in liable data aggregation

Internet of Things Based Reconfigurable SIMD Processor for High-Speed End Devices in FPGA

A Survey of Data Aggregation Protocols for Energy Conservation in WSN and IoT

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