Energy Consumption Overhead Research Articles

OcAPO: Fine-grained occupancy-aware, empirically-driven PDC control in open-plan, shared workspaces

Passive Displacement Cooling (PDC) is a relatively recent technology gaining attention as a means of significantly reducing building energy consumption overheads, especially in tropical climates. PDC eliminates the use of mechanical fans, instead using chilled-water heat exchangers to perform convective cooling. In this paper, we identify and characterize the impact of several key parameters affecting occupant comfort in a 1000m2 open-floor area (consisting of multiple zones) of a ZEB (Zero Energy Building) deployed with PDC units and tackle the problem of setting the temperature setpoint of the PDC units to assure occupant thermal comfort and yet conserve energy. We tackle two key practical challenges: (a) the zone-level (i.e., occupant-experienced) temperature differs significantly, depending on occupancy levels, from that measured by the ceiling-mounted thermal sensors that drive the PDC control loop, (b) sparsely deployed sensors are unable to capture the often-significant differences in ambient temperature across neighboring zones. Using extensive real-world coarser-grained measurement data (collected over 60 days under varying occupancy conditions), (a) we first uncover the various parameters that affect the occupant-level ambient temperature, and then (b) devise a trace-based model that helps identify the optimum combination of PDC setpoints, collectively across multiple zones, while accommodating variations in the occupancy levels and weather conditions. Using this trace-based model, our OcAPO system can assure ambient temperature experienced by occupants within a tolerance of 0.3°C. In contrast, the existing approach of occupancy-agnostic, rule-based setpoint control violates this tolerance interval more than 80% of the time. However, this initial model requires unnecessary and continual database lookups and is unable to derive finer-grained setpoints, thereby potentially missing opportunities for additional energy savings. We thus collected data for another 15 days, with finer-grained setpoint control in increments of 0.2∘ under varying occupancy conditions in the second phase. To determine PDC setpoints efficiently, we subsequently used the empirical data to train a KNN-based regression model. Additional studies on our real-world testbed demonstrate the regressor-based OcAPO approach is able to assure occupant-level ambient temperature within a narrow 0.2°C tolerance. We also demonstrate that the regression version of OcAPO can reduce the opening percentage of PDC valves (an indirect proxy for energy consumption) by 58.9% under low occupancy compared to the trace-based model.

ECA-VFog: An efficient certificateless authentication scheme for 5G-assisted vehicular fog computing.

Fifth-generation (5G)-enabled vehicular fog computing technologies have always been at the forefront of innovation because they support smart transport like the sharing of traffic data and cooperative processing in the urban fabric. Nevertheless, the most important factors limiting progress are concerns over message protection and safety. To cope with these challenges, several scholars have proposed certificateless authentication schemes with pseudonyms and traceability. These schemes avoid complicated management of certificate and escrow of key in the public key infrastructure-based approaches in the identity-based approaches, respectively. Nevertheless, problems such as high communication costs, security holes, and computational complexity still exist. Therefore, this paper proposes an efficient certificateless authentication called the ECA-VFog scheme for fog computing with 5G-assisted vehicular systems. The proposed ECA-VFog scheme applied efficient operations based on elliptic curve cryptography that is supported by a fog server through a 5G-base station. This work conducts a safety analysis of the security designs to analysis the viability and value of the proposed ECA-VFog scheme. In the performance ovulation section, the computation costs for signing and verification process are 2.3539 ms and 1.5752 ms, respectively. While, the communication costs and energy consumption overhead of the ECA-VFog are 124 bytes and 25.610432 mJ, respectively. Moreover, comparing the ECA-VFog scheme to other existing schemes, the performance estimation reveals that it is more cost-effective with regard to computation cost, communication cost, and energy consumption.

Coverage and throughput analysis of an energy efficient UAV base station positioning scheme

Recently, the use of unmanned aerial vehicles (UAVs) for wireless communications has attracted much research attention. However, most applications of UAVs for wireless communication provisioning are not feasible as researchers fail to consider some vital aspects of their deployment, especially the energy requirements of both the UAV and communication system. The considerable energy consumption overhead involved in flying or hovering UAVs makes them less appealing for green wireless communications. Therefore, in this work, we examine the feasibility of an alternative energy-efficient deployment scheme where UAVs can be made to land-on designated locations, also known as landing stations (LSs). The idea of LS makes the UAV-based wireless communication more durable and advantageous, since the total energy consumption is reduced by minimizing the flying/hovering energy consumption, which, in turn, enables diverse set of applications including emergency and pop-up networking. We evaluate the impact of the separation distance between these LSs and the Optimal Hovering Position (OHP) on the network performance. Specifically, we develop mathematical frameworks to model the relationship between UAV power consumption, coverage probability, throughput, and separation distance. Numerical results reveal that a significant energy reduction can be achieved when the LS concept is exploited with a slight compromise in coverage probability and throughput. However, the choice of a suitable LS location depends on the users’ service requirements, transmit power, and frequency band utilized.

Efficient hybrid centralized and blockchain-based authentication architecture for heterogeneous IoT systems

With the rapid increase in the number of Internet of Things (IoT) devices in recent years, massive amounts of sensitive IoT data are being generated and transmitted over the Internet. Despite its growing adoption in various fields, IoT security remains a major challenge requiring further research. IoT authentication is an essential security mechanism for building trust in IoT systems. However, conventional authentication approaches use expensive cryptographic primitives that do not align with the resource-constrained nature of IoT devices. Furthermore, centralized authentication schemes have proven to be inapplicable for cross-domain authentication and do not limit the scalability of IoT networks. Recently, blockchain technology has been applied to building decentralized authentication between IoT devices. Nevertheless, most existing blockchain-based authentication approaches incur high overhead in IoT computation, storage, and energy consumption. Authentication time is another critical issue in real-time IoT systems. When numerous IoT authentication requests are transferred to the blockchain, an additional time delay is imposed, in addition to the high computational cost of the blockchain caused by the consensus mechanism. This study proposes a hybrid centralized and blockchain-based authentication architecture for IoT systems. Edge servers are deployed to provide centralized authentication for associated IoT devices. A blockchain network of centralized edge servers is then established to ensure decentralized authentication and verification of IoT devices that belong to different and heterogeneous IoT systems. Lightweight cryptographic methods are implemented to achieve efficient authentication, in which limiting the consumption of IoT resources is required. The architecture is demonstrated using a local Ethereum blockchain network. The results indicate that the proposed method achieves significant improvements in terms of computation cost, execution time, and power consumption for IoT compared with centralized and blockchain-based authentication schemes. A security analysis proves the ability of our architecture to mitigate attacks and satisfy the IoT security requirements.

Deep Reinforcement Learning-Based Task Offloading for Parked Vehicle Cooperation in Vehicular Edge Computing

Vehicular edge computing (VEC) has greatly enhanced the quality of vehicle service with low latency and high reliability. However, in some areas not covered by roadside infrastructures or in cases when the infrastructures are damaged or fail, the offloaded tasks cannot have the chance to be performed. Even in the areas deployed with infrastructures, when a large number of offloaded tasks are generated, the edge servers may not be capable of processing them in time, owing to their computing resources constraint. Based on the above observations, we proposed the idea of parked vehicle cooperation in VEC, which uses roadside parked vehicles with underutilized computational resources to cooperate with each other to perform the compute-intensive tasks. Our approach aims to overcome the challenge brought by infrastructure lacking or failure and make up for the shortage of computing resources in VEC. In our approach, firstly, the roadside parked vehicles are managed as different parking clusters. Then, the optimal amount of resources required for each offloaded task is analyzed. Furthermore, a task offloading algorithm based on deep reinforcement learning (DRL) is proposed to minimize the total cost, which is composed of the task execution delay and the energy consumption overhead of the parked vehicles for executing the task. A large number of simulation results show that, compared with other algorithms, our approach not only has the highest task completion execution successful rate, but also has the lowest task execution cost.

Near-Optimal Energy Management for Energy Harvesting IoT Devices Using Imitation Learning

Internet of Things (IoT) devices are becoming popular in a number of transformative applications, including smart health, digital agriculture, and wide area sensing. However, small battery capacities and the need for frequent battery replacements or recharging have hindered their widespread adoption. Energy harvesting (EH) and management present a promising opportunity to enable long-term recharge free operation of IoT devices. State-of-the-art energy management approaches employ dynamic optimization methods to manage the harvested energy. However, the dynamic optimization methods are typically computationally intensive and lead to significant energy overhead for energy-constrained IoT devices. In contrast, this article proposes an imitation learning (IL)-based energy management algorithm that provides the energy budget or allocation for each decision interval without the need for dynamic optimization. We present an efficient approach to design Oracle policies that optimize the energy allocation of the IoT device to enable self-powered operation while maximizing the utility to the application. Then, we leverage the Oracle policy to train an online policy that performs near-optimal energy allocation at runtime. Our experiments with solar EH data for six years from three locations show that the proposed IL policies achieve allocation that is, on average, within 2.5 J of the Oracle, while having an energy consumption overhead of 154 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu \text{J}$ </tex-math></inline-formula> .

Efficient mitigation technique for Black Hole router attack in Network-on-Chip

The Multiprocessor System-on-Chip (MPSoC) has widely engaged in embedded systems. The MPSoC is mainly composed of multi-cores connected through an on-chip interconnection, Known as Network-on-Chip (NoC), which offers an efficient and scalable interconnection platform. The MPSoC presents a large degree of parallelism, where several applications are executing on multiple processors sharing the same NoC platform. So, it has made the NoC a focal point for potential security attacks. In the modern semiconductor industry, many different parties take part in the system design. As a result, the NoC could be infected with a malicious circuit, known as Hardware Trojan (HT), to apply a Denial-of-Service (DoS) attack.In this article, an HT threat model that applies a DoS attack by deliberately discarding the packets from the NoC is presented. The infected router that drops the packets is also known as Black Hole Router (BHR). A secure interconnection network against the BHR attack is presented. The proposed technique can detect, locate the BHR in runtime, and isolate it from the network routing by detouring the packets around the infected router. The designed model has been extended to reduce energy consumption during the BHR detection process. The experimental results demonstrate that the proposed energy-efficient runtime BHR detection has 1% and 2% throughput and energy consumption overheads, respectively.

An Architectural-Level Reliability Improvement Scheme in STT-MRAM Main Memory

DRAM technologies in main memory of computer systems face fundamental scalability challenges beyond 10 nm node. In recent years, Spin-Transfer Torque Magnetic Random-Accesses Memory (STT-MRAM) has attracted much attention as an alternative to DRAM in main memory. Storing data as a resistance state instead of an electric charge solves the issues with DRAMs. Non-volatility, near-zero static power dissipation, high speed, and high endurance are some of the advantages of STT-MRAMs. However, STT-MRAMs suffer from problems with write and retention failures due to their stochastic nature. The data retention failure probability at the main memory level is high due to long data idle times because of the multiple cache levels and long distance from the processor registers. In addition, if the write failure occurs in one bit, the entire data block is erroneous, so the write failure cannot be ignored as well. This study improves the reliability of main memory at two levels of memory with the minimum change in the architecture. At the first level, the data block is prepared before writing to memory, and the main cause of write failure, which is the 0→1 transitions, is completely eliminated. In the second level, the data blocks returned to main memory are managed to mitigate the data retention failure and to reduce the idle time intervals of data blocks. The simulation results show that the proposed scheme reduces the write failure probability by 95.8% and the data retention failure by 33.6%. The side-effect of these reductions is imposing only 1.5% energy consumption overhead and 1.2% performance degradation on the system.

Smart Architecture Energy Management through Dynamic Bin-Packing Algorithms on Cloud

Smart Home Architecture is suitable for progressive and symmetric urbanization. Data being generated in smart home appliances using internet of things should be stored in cloud where computing resources can analyze the data and generate the decisive pattern within no time. This additional requirement of storage, majorly, comprising of unfiltered data escalates requirement of host machines which carries with itself extra overhead of energy consumption; thus, extra cost has to be beard by service providers. Various static algorithms are already proposed to improve energy management of cloud data centers by reducing number of active bins. These algorithms are not able to cater to the needs of present heterogeneous requests generated in cloud machines by people of diversified work environment with adhering to the requirements of quality parameters. Therefore, the paper has proposed and implemented dynamic bin-packing approaches for smart architecture that can significantly reduce energy consumption without compromising upon makespan, resource utilization and Quality of Service (QoS) parameters. The novelty of the proposed dynamic approaches in comparison to the existing static approaches is that the proposed approach dynamically creates and dissolves virtual machines as per incoming and completed requests which is a dire need of present computing paradigms via attachment of time-frame with each virtual machine. The simulations have been performed on JAVA platform and dynamic energy utilized-best fit decreasing bin packing technique has produced better results in maximum runs.

A Stateless Spatial IPv6 Address Configuration Scheme for Internet of Things

The Internet of Things (IoT) is going to create a heterogeneous network of billions or trillions of connected things with self-aware, self-configuration capabilities that will exchange data with each other. This heterogeneous IoT network requires standard protocols, secure and energy-efficient wireless communication, and the allocation of unique identities such as IPv6 addresses. One of the key challenges in such a heterogeneous network is the maintenance of device location and unique identity to ensure the smooth functioning of location-aware applications. Hence, this paper presents a stateless spatiotemporal IPv6 addressing scheme called four-dimensional IP addressing (FDIPA). It is founded on the idea that two objects can not be in the same place at the same time. It uses the time and location information of the device to generate the target IPv6 address and assigns it to the device after verifying its uniqueness with the duplicate address detection (DAD) protocol. It not only maintains spatial and temporal information but also guarantees a unique identity for a large-sized network. Analytically, it is proved that FDIPA will always generate a unique identity if there is no technological limitation. However, the performance of FDIPA is evaluated based on metrics such as Assignment Success Rate (ASR), communication overhead, and total energy consumption in the assignment of a unique IPv6 address. The experimental results show that FDIPA achieves good ASR while keeping low energy consumption and less overhead in the network.

Efficient selective replication of critical code regions for SDC mitigation leveraging redundant multithreading

Redundant multithreading (RMT) is an effective reliability solution that provides thread-level replication; however, it imposes additional overheads in terms of performance loss or energy consumption. Partial-RMT is an alternative solution that provides partial redundancy of an executing thread to reduce such overheads while trading off full coverage from faults. In this study, we propose a software-level RMT approach that offers lightweight replication of partial code regions within the same application process. Our software-level RMT approach is particularly suitable for applications with varying code criticality, where we determine the critical code regions by performing a fault injection campaign in addition to execution time profile analysis. Using the results of the previous step, the application programmer annotates the source code to indicate the specific code regions that should be executed redundantly without re-implementing the application program from scratch. Our lightweight software-level RMT tool improves the average silent data corruption (SDC) rate of 30 applications of the PolyBench benchmark suite by around 7.6\(\times\) with average performance and energy consumption overheads of 22 and 37%, respectively, compared to the original version of the program.

A novel method of mobility-based clustering protocol in software defined sensor network

The distributed clustering method is widely used to extend network lifetime in traditional wireless sensor networks. However, it is difficult to achieve the idea performance of the whole network, such as transmission rate, energy consumption, and control overhead, neglecting the global stability of the network. To tackle this problem, a centralized mobility-based clustering (CMBC) protocol is proposed in the software defined sensor network. The method of CMBC mainly consists of two aspects: first, CMBC clusters the nodes with the connection time between the mobile nodes (i.e., noncluster head nodes, non-CH) and the cluster head (CH) and establishes stable topological structures between the non-CHs and the CH; second, when emergencies occur, the centralization management control center sends the configuration files to replace the CH. Compared to the distributed network of MBC, WCRA and IMP-MECA protocol, the proposed method can be applied in scenarios with high-speed mobile nodes to improve the network performance in terms of transmission successful rate, average power consumption, and average control overhead.

A Novel DRAM Architecture for Improved Bandwidth Utilization and Latency Reduction Using Dual-Page Operation

Emerging memory-intensive applications require a paradigm shift from processor-centric to memory-centric computing. The performance of state-of-the-art computing systems and accelerators designed for such applications is not limited by the processing speed but rather by the limited DRAM bandwidth and long DRAM latencies. Although, the interface frequency of new commodity DRAM memories is constantly increasing to achieve a higher peak bandwidth, this bandwidth cannot be fully utilized due to long internal latencies. Page-misses (i.e., change of active row) are one of the key contributors to long access latencies that result in a low bandwidth utilization. In this brief, we propose a novel DRAM sub-array level architecture referred to as Dual-Sense-Amplifier (DSA) that masks the page-miss latency by allowing individual sub-arrays or banks to flexibly open two rows concurrently. Additionally, the DSA architecture design aim to be fully compatible with any type of commodity DRAM architecture (e.g., HBM, GDDR, etc.) and to retain most of its circuit designs. The area overhead of DSA for an 8Gb device is 9.6% compared to a commodity DRAM device. On average, a DSA 8Gb device improves the bandwidth utilization by 17.53% and reduces the average response latency by 31.59 ns compared to commodity DRAM devices operated at 800 MHz. Moreover, we demonstrate that the energy consumption overhead of a DSA DRAM in comparison to a commodity DRAM is negligible.

Reduced Precision DWC: An Efficient Hardening Strategy for Mixed-Precision Architectures

Duplication with Comparison (DWC) is an effective software-level solution to improve the reliability of computing devices. However, it introduces performance and energy consumption overheads that could be unsuitable for high-performance computing or real-time safety-critical applications. In this article, we present Reduced-Precision Duplication with Comparison (RP-DWC) as a means to lower the overhead of DWC by executing the redundant copy in reduced precision. RP-DWC is particularly suitable for modern mixed-precision architectures, such as NVIDIA GPUs, that feature dedicated functional units for computing with programmable accuracy. We discuss the benefits and challenges associated with RP-DWC and show that the intrinsic difference between the mixed-precision copies allows for detecting most, but not all, errors. However, as the undetected faults are the ones that fall into the difference between precisions, they are the ones that produce a much smaller impact on the application output and, thus, might be tolerated. We investigate RP-DWC impact into fault detection, performance, and energy consumption on Volta GPUs. Through fault injection and beam experiment, using three microbenchmarks and four real applications, we show that RP-DWC achieves an excellent coverage (up to 86 percent) with minimal overheads (as low as 0.1 percent time and 24 percent energy consumption overhead).

Efficient and Stateless P2P Routing Mechanisms for the Internet of Things

Arbitrary point-to-point (P2P) routing is becoming a necessity in a multitude of Internet-of-Things (IoT) applications, including building automation, smart cities, and smart manufacturing. For this reason, new P2P routing protocols such as lightweight on-demand ad hoc distance-vector routing protocol next generation (LOADng) and ad hoc on-demand distance vector routing-based RPL protocol (AODV-RPL) are being standardized. Both protocols are inspired by AODV, and hence, they might suffer from broadcast storms caused by flooding route requests (RREQs) and related issues. Thus, while AODV-RPL takes advantage of RPL mechanisms, LOADng uses blind flooding to forward RREQs. However, both lack effective techniques for avoiding unnecessary RREQ transmissions when routes are found. In this article, we first deploy stopping Trickle timers to ensure scalable, efficient, and reliable dissemination of RREQs. Second, we devise and present new techniques to suppress unnecessary RREQs with optimizations for radio duty-cycled networks. Finally, the two mechanisms are combined in a third proposal for better efficiency. These mechanisms provide individually and collectively great enhancements to P2P routing protocols, such as AODV-RPL and LOADng, while staying backward compatible with their base specifications. The performance of the proposed mechanisms when applied to LOADng has been validated using both extensive time-accurate simulations and large-scale public testbeds. The obtained results have shown the effectiveness of the proposed mechanisms with around 50% less overhead and savings in energy consumption, along with a 20% gain in route discovery ratio at the expense of an increase in discovery delays.

A Flexible Enhanced Throughput and Reduced Overhead (FETRO) MAC Protocol for ETSI SmartBAN

Smart body area networks (SmartBAN) is an emerging wireless body area networks (WBAN) standard proposed by the European Telecommunications Standards Institute (ETSI). This paper first examines the potential of SmartBAN medium access control (MAC) layer with scheduled access to support a myriad of WBAN applications, having diverse data rate requirements. Extra scheduled access slots can be allocated to high date rate sensor nodes for managing their data rate requirements. High data rate sensor nodes can also be re-assigned to use the available time slots of low data rate sensor nodes in Inter-Beacon Interval (IBI) by the central hub. But these two schemes incorporate different physical (PHY) and MAC layer overheads related to frame transmission, frame acknowledgement and slot re-assignment. This redundant overhead transmission results in high overhead energy consumption and reduced effective throughput. Therefore, an innovative and flexible enhanced throughput and reduced overhead (FETRO) MAC protocol for scheduled access is proposed in this article. In the proposed scheme, the sensor node data rate requirements are considered while assigning the scheduled access slot duration by allowing minimal changes in the base-line standard implementation. This infers the provision of scheduled access slots with variable slot durations within an IBI. We also evaluate the existing techniques of extra slot allocation and slot re-assignment in SmartBAN as well as the proposed FETRO MAC protocol with variable slot length. The proposed FETRO MAC scheme results in optimizing both the overall throughput and normalized overhead energy consumption per kilo bits per second (Kbps). Additionally, the impact of various WBAN channel models over these throughput management approaches is also investigated. The proposed FETRO MAC protocol with variable slot duration gives an average reduction of 65.5 and 59.16 percent, respectively, in the hub and nodes normalized overhead energy consumption per Kbps outcomes, as compared to the de-facto SmartBAN MAC scheduling strategies.

Energy Consumption Performance of Opportunistic Device-to-Device Relaying Under Log-Normal Shadowing

Efficient transmission protocols are required to minimize the energy consumption of mobile devices for ubiquitous connectivity in the next-generation of wireless networks. In this article, we analyze the energy consumption performance of a two-hop opportunistic device-select relaying (ODSR) scheme, where a device can either transmit data directly to a base station (BS) or relay the data to a nearby device, which forwards the data to the BS. We select a single device opportunistically from a device-to-device (D2D) network based on the energy required for transmission, including the energy consumed in the circuitry of the devices. By considering the log-normal shadowing as the dominant factor between devices and the BS, and Rayleigh fading in D2D links, we derive analytical bounds and scaling laws on average energy consumption. The derived analytical expressions show that the energy consumption of the ODSR decreases logarithmically with an increase in the number of devices, and achieves near-optimal performance only with a few nearby devices. This is an important design criterion to reduce latency and overhead energy consumption in a relay-assisted large-scale network. We also demonstrate the performance of the ODSR using simulations in realistic scenarios of a wireless network.

TSE: Two-Step Elimination for MLC STT-RAM Last-Level Cache

Spin-transfer torque RAM (STT-RAM) is an emerging non-volatile memory that has been recognized as the potential candidate to replace SRAM. Compared with SRAM, STT-RAM has advantages of non-volatility, zero leakage power, and higher density. To further improve data density, multi-level cell (MLC) STT-RAM that can store two bits per cell has been proposed. However, writing hard bit of a cell would write its soft bit to the same value as well, which complicates the write operation of MLC STT-RAM. Although two-step transition (TT) is usually adopted to ensure the data correctness during a write operation, it incurs overhead of additional energy consumption and performance degradation. In this article, we propose the two-step elimination (TSE) scheme to eliminate TTs while ensure data integrity. By flipping hard bits of the cells that suffered from TTs, the TSE scheme could reduce TTs to soft transitions (STs) or zero transitions (ZTs), which incur much less overhead than TTs. To keep track of the flipped cells effectively, 6-bit TSE tag is introduced. We exploit tag reversing and advanced mode of the TSE scheme to further improve the performance. The experimental results showed that our scheme could reduce 61 percent TTs and achieve significant lifetime improvement, compared with conventional MLC STT-RAM (CMLC) scheme.

Deployment and Selection of Monitoring Nodes for Detection of Selfish Attacks in MANET

Since Mobile Ad hoc Network (MANET) has distributed network structure using wireless links, designing efficient security applications has become a critical need. Selfish nodes are nodes that refuse to forward the data from other nodes. The existence of selfish nodes will disturb the normal process of the network, and reduce the network performance. Intrusion Detection System (IDS) is a scheme for detecting any misbehaviors in the network operation by monitoring the traffic flow. Each monitoring node need to execute the IDS module. The common problems encountered by the monitoring nodes are energy depletion, link disconnection, mobility and coverage. Hence the selection of monitoring nodes plays an important role in IDS. This paper develops a technique for deployment and selection of monitoring nodes for detection of selfish attacks. In this technique, the whole network is virtually divided in smaller grid like zones. In each grid, the nodes with higher stability and better coverage are assigned a reward value. A cost metric is derived in terms of energy consumption and computational delay. Then the nodes with minimum cost and high reward are selected as monitoring nodes. By simulation results, it is shown that the proposed technique has reduced detection delay, energy consumption and detection overhead.

An enhanced soft computing-based formulation for secure data aggregation and efficient data processing in large-scale wireless sensor network

Rapid growth in wireless technologies and communication, wireless sensor network (WSN) skills, data gathering and management models has paved the sensor technology a great impact on all factors of human life. In WSN, maximum consumption of constrained resources is considered to be the major challenge. Additionally, secure data aggregation has made the research domain more interesting. For consuming the limited sensor node resources optimally, data aggregation model plays a vital role. It reduces the redundant and unwanted data transmission and enhances the accuracy of data, thereby reducing the energy consumption rate and consumption overhead. Hence, for balancing the energy efficient data processing with secure data aggregation in large-scale WSN, optimized security model using enhanced fully homomorphic encryption (OSM-EFHE) has been developed in this work. First, the network is divided into clusters and cluster head which acts as an aggregator is selected based on the fuzzy if–then rule which helps in consumption of energy. Second, it provides data confidentiality and maintains subjective aggregation functions through fully homomorphic encryption (FHE). In this work, Van Dijk, Gentry, Halevi and Vaikunathan key generation plan with public key compression is used which condenses the public key dimension which is one of the major computations overhead for FHE. Finally, data integrity operation has also been induced with message authentication code. When comparing with the existing approaches, simulation results make a clear note of average delay of the network as 1.2 ms and a higher throughput of 4500 bps approximately. Thus, the overall transmission of data has been increased by means of employing OSM-EFHE model.