Biblio

Cloud computing forms the backbone of the era of automation and the Internet of Things (IoT). It offers computing and storage-based services on consumption-based pricing. Large-scale datacenters are used to provide these service and consumes enormous electricity. Datacenters contribute a large portion of the carbon footprint in the environment. Through virtual machine (VM) consolidation, datacenter energy consumption can be reduced via efficient resource management. VM selection policy is used to choose the VM that needs migration. In this research, we have proposed PbV mSp: A priority-based VM selection policy for VM consolidation. The PbV mSp is implemented in cloudsim and evaluated compared with well-known VM selection policies like gpa, gpammt, mimt, mums, and mxu. The results show that the proposed PbV mSp selection policy has outperformed the exisitng policies in terms of energy consumption and other metrics.

Homomorphic encryption (HE) facilitates computing over encrypted data without using the secret keys. It is currently inefficient for practical implementation on the Internet of Things (IoT). However, the performance of these HE schemes may increase with optimized libraries and hardware capabilities. Thus, implementing and analyzing HE schemes and protocols on resource-constrained devices is essential to deriving optimized and secure schemes. This paper develops an energy profiling framework for homomorphic encryption on IoT devices. In particular, we analyze energy consumption and performance such as CPU and Memory utilization and execution time of numerous HE schemes using SEAL and HElib libraries on the Raspberry Pi 4 hardware platform and study energy-performance-security trade-offs. Our analysis reveals that HE schemes can incur a maximum of 70.07% in terms of energy consumption among the libraries. Finally, we provide guidelines for optimization of Homomorphic Encryption by leveraging multi-threading and edge computing capabilities for IoT applications. The insights obtained from this study can be used to develop secure and resource-constrained implementation of Homomorphic encryption depending on the needs of IoT applications.

Modular exponentiation (ME) is a complex operation for several public-key cryptosystems (PKCs). Moreover, ME is expensive for resource-constrained devices in terms of computation time and energy consumption, especially when the exponent is large. ME is defined as the task of raising an integer x to power k and reducing the result modulo some integer n. Several methods to calculate ME have been proposed. In this paper, we present the efficient ME methods. We then implement the methods using different security levels of RSA keys on a Raspberry Pi. Finally, we give the fast ME method.

Encryption defined as change information process (which called plaintext) into an unreadable secret format (which called ciphertext). This ciphertext could not be easily understood by somebody except authorized parson. Decryption is the process to converting ciphertext back into plaintext. Deoxyribonucleic Acid (DNA) based information ciphering techniques recently used in large number of encryption algorithms. DNA used as data carrier and the modern biological technology is used as implementation tool. New encryption algorithm based on DNA is proposed in this paper. The suggested approach consists of three steps (conventional, stream cipher and DNA) to get high security levels. The character was replaced by shifting depend character location in conventional step, convert to ASCII and AddRoundKey was used in stream cipher step. The result from second step converted to DNA then applying AddRoundKey with DNA key. The evaluation performance results proved that the proposed algorithm cipher the important data with high security levels.

Nowadays, lives are very much easier with the help of IoT. Due to lack of protection and a greater number of connections, the management of IoT becomes more difficult To manage the network flow, a Software Defined Networking (SDN) has been introduced. The SDN has a great capability in automatic and dynamic distribution. For harmful attacks on the controller a centralized SDN architecture unlocks the scope. Therefore, to reduce these attacks in real-time, a securing SDN enabled IoT scenario infrastructure of Fog networks is preferred. The virtual switches have network enforcement authorized decisions and these are executed through the SDN network. Apart from this, SDN switches are generally powerful machines and simultaneously these are used as fog nodes. Therefore, SDN looks like a good selection for Fog networks of IoT. Moreover, dynamically distributing the necessary crypto keys are allowed by the centralized and software channel protection management solution, in order to establish the Datagram Transport Layer Security (DTIS) tunnels between the IoT devices, when demanded by the cyber security framework. Through the extensive deployment of this combination, the usage of CPU is observed to be 30% between devices and the latencies are in milliseconds range, and thus it presents the system feasibility with less delay. Therefore, by comparing with the traditional SDN, it is observed that the energy consumption is reduced by more than 90%.

Using multi-UAV systems to accomplish both civil and military missions is becoming a popular trend. With the development of software and hardware technologies, Unmanned aerial vehicles (UAVs) are now able to operate autonomously at edge. However, the remote control of manned systems, e.g., ground control station (GCS), remains essential to mission success, and the system's control and non-payload communication (CNPC) are facing severe cyber threats caused by smart attacks. To avoid hijacking, in this paper, we propose a secure mechanism that reduces such security risks for multi-UAV systems. We introduce friendly jamming from UAVs to block eavesdropping on the remote control channel. The trade-off between security and energy consumption is optimized by three approaches designed for UAV and GCS under algorithms of different complexities. Numerical results show the approach efficiency under different mission conditions and security demands, and demonstrate the features of the proposed mechanism for various scenarios.

With their variety of application verticals, smart cities represent a killer scenario for Cloud-IoT computing, e.g. fog computing. Such applications require a management capable of satisfying all their requirements through suitable service placements, and of balancing among QoS-assurance, operational costs, deployment security and, last but not least, energy consumption and carbon emissions. This keynote discusses these aspects over a motivating use case and points to some open challenges.

Integration of technology with power grid emerged Smart grid. The advancement of power grid into smart grid faces some security issues like message mod-ification attacks, message injection attacks etc. If these issues are correctly not addressed, then the performance of the smart grid is degraded. Smart grid has bidirectional communication among the smart grid entities. The flow of user energy consumption information between all smart grid entities may lead the user privacy violation. Smart grids have various components but service providers and smart meters are the main components. Smart meters have sensing and communication functionality, while service providers have control and communication functionality. There are many privacy preservation schemes proposed that ensure the cus-tomer's privacy in the smart grid. To preserve the customer's data privacy and communication, authentication and key agreement schemes are required between the smart meter and the service provider. This paper proposes an efficient key agreement protocol to handle several security challenges in smart grid. The proposed protocol is tested against the various security attributes necessary for a key establishment protocol and found safe. Further the performance of the proposed work is compared with several others existing work for smart grid application and it has been observed that the proposed protocol performs significantly better than the existing protocols available in the literature.

The concept of a microgrid has emerged as a promising solution for the management of local groups of electricity consumers and producers. The use of end-users' energy usage data can help in increasing efficient operation of a microgrid. However, existing data-aggregation schemes for a microgrid suffer different cyber attacks and do not provide high level of accuracy. This work aims at designing a privacy-preserving data-aggregation scheme for a microgrid of prosumers that achieves high level of accuracy, thereby benefiting to the management and control of a microgrid. First, a novel smart meter readings data protection mechanism is proposed to ensure privacy of prosumers by hiding the real energy usage data from other parties. Secondly, a blockchain-based data-aggregation scheme is proposed to ensure privacy of the end-users, while achieving high level of accuracy in terms of the aggregated data. The proposed data-aggregation scheme is evaluated using real smart meter readings data from 100 prosumers. The results show that the proposed scheme ensures prosumers' privacy and achieves high level of accuracy, while it is secure against eavesdropping and man-in-the-middle cyber attacks.

When applied to short-term energy consumption forecasting, the federated learning framework allows for the creation of a predictive model without sharing raw data. There is a limit to the accuracy achieved by standard federated learning due to the heterogeneity of the individual clients' data, especially in the case of electricity data, where prediction of peak demand is a challenge. A set of clustering techniques has been explored in the literature to improve prediction quality while maintaining user privacy. These studies have mainly been conducted using sets of clients with similar attributes that may not reflect real-world consumer diversity. This paper explores, implements and compares these clustering techniques for privacy-preserving load forecasting on a representative electricity consumption dataset. The experimental results demonstrate the effects of electricity consumption heterogeneity on federated forecasting and a non-representative sample's impact on load forecasting.

Traditional power consumption management systems are not showing enough reliability and thus, smart grid technology has been introduced to reduce the excess power wastages. In the context of smart grid systems, network communication is another term that is used for developing the network between the users and the load profiles. Cloud computing and clustering are also executed for efficient power management. Based on the facts, this research is going to identify wireless network communication systems to monitor and control smart grid power consumption. Primary survey-based research has been carried out with 62 individuals who worked in the smart grid system, tracked, monitored and controlled the power consumptions using WSN technology. The survey was conducted online where the respondents provided their opinions via a google survey form. The responses were collected and analyzed on Microsoft Excel. Results show that hybrid commuting of cloud and edge computing technology is more advantageous than individual computing. Respondents agreed that deep learning techniques will be more beneficial to analyze load profiles than machine learning techniques. Lastly, the study has explained the advantages and challenges of using smart grid network communication systems. Apart from the findings from primary research, secondary journal articles were also observed to emphasize the research findings.

For the Internet of things (IoT) secure data aggregation issues, data privacy-preserving and limited computation ability and energy of nodes should be tradeoff. Based on analyzing the pros-and-cons of current works, a low energy- consuming secure data aggregation method (LCSDA) was proposed. This method uses shortest path principle to choose neighbor nodes and generates the data aggregation paths in the cluster based on prim minimum spanning tree algorithm. Simulation results show that this method could effectively cut down energy consumption and reduce the probability of cluster head node being captured, in the same time preserving data privacy.

The aim of this paper is to describe the battery lifetime estimation and energy consumption model of the sensor nodes in TDMA wireless mesh sensor using merged data collecting (MDC) methods based on lithium thionyl chloride batteries. Defining the energy consumption of the nodes in wireless mesh networks is crucial for battery lifetime estimation. In this paper, we describe the timing, energy consumption, and battery lifetime estimation of the MDC method in the TDMA mesh sensor networks using flooding routing. For the battery life estimation, we made a semiempirical model that describes the energy consumption of the nodes with a real battery model. In this model, the low-level constraints are based on the measured energy consumption of the sensor nodes in different operation phases.

This paper presents an energy consumption model of the sensor nodes in TDMA wireless mesh sensor network using merged data collecting (MDC) methods. Defining the energy consumption of the nodes in wireless mesh networks is crucial for battery lifetime estimation. In this paper, we describe the semiempirical model of the energy consumption of MDC method in the TDMA mesh sensor networks using flooding routing. In the model the low-level constraints are based on the measured energy consumption of the sensor nodes in the different operation phases.

ETSI DECT-2020 New Radio (NR) is a new flexible radio interface targeted to support a broad range of wireless Internet of Things (IoT) applications. Recent reports have shown that DECT-2020 NR achieves good delay performance and it has been shown to fulfill both massive machine-type communications (mMTC) and ultra-reliable low latency communications (URLLC) requirements for 5th generation (5G) networks. A unique aspect of DECT-2020 as a 5G technology is that it is an autonomous wireless mesh network (WMN) protocol where the devices construct and uphold the network independently without the need for base stations or core network architecture. Instead, DECT-2020 NR relies on part of the network devices taking the role of a router to relay data through the network. This makes deployment of a DECT-2020 NR network affordable and extremely easy, but due to the nature of the medium access protocol, the routing responsibility adds an additional energy consumption burden to the nodes, who in the IoT domain are likely to be equipped with a limited battery capacity. In this paper, we analyze by system level simulations the energy consumption of DECT-2020 NR networks with different network sizes and topologies and how the reported low latencies can be upheld given the energy constraints of IoT devices.

The major energy-hungry components in today's battery-operated embedded devices are mostly peripheral modules like LTE, WiFi, GPS, etc. Inefficient use of these modules causes energy hotspots, namely segments of the embedded software in which the module wastes energy. We study two such hotspots in the current paper, and provide the corresponding detection and removal algorithms based on static analysis techniques. The program code hotspots occur due to unnecessary releasing and re-acquiring of a module (which puts the module in power saving mode for a while) and misplaced acquiring of the module (which makes the module or processor to waste energy in idle mode). The detections are performed according to some relation between extreme (worst-case/best-case) execution times of some program segments and time/energy specifications of the module. The experimental results on our benchmarks show about 28 percent of energy reduction after the hotspot removals.

Internet of Things (IoT) is becoming an emerging paradigm to provide pervasive connectivity where “anything“ can be connected “anywhere” at “anytime” via massive deployment of physical objects like sensors, controllers, and actuators. However, the open nature of wireless communications and the energy constraint of the IoT devices impose strong security concerns. In this context, traditional cryptographic techniques may not be suitable in such a resource-constrained network. To address this problem, an effective security solution that ensures a trade-off between security effectiveness and energy efficiency is required. In this paper, we exploit cooperative transmission between sensor nodes in IoT for e-Health application, as a promising technique to enhance the physical layer security of wireless communications in terms of secrecy capacity while considering the resource-impoverished devices. Specifically, we propose a dynamic and cooperative virtual multiple-input and multiple-output (MIMO) configuration approach based on game theory to preserve the confidentiality of the transmitted messages with high energy savings. For this purpose, we model the physical layer security cooperation problem as a non-transferable coalition formation game. The set of cooperative devices form a virtual dynamically-configured MIMO network that is able to securely and efficiently transmit data to the destination. Simulation results show that the proposed game-based virtual MIMO configuration approach can improve the average secrecy capacity per device as well as the network lifetime compared to non-cooperative transmission.

Mobile edge computing (MEC) emerges recently to help process the computation-intensive and delay-sensitive applications of resource limited mobile devices in support of MEC servers. Due to the wireless offloading, MEC faces many security challenges, like eavesdropping and privacy leakage. The anti-eavesdropping offloading or privacy preserving offloading have been studied in existing researches. However, both eavesdropping and privacy leakage may happen in the meantime in practice. In this paper, we propose a privacy preserved secure offloading scheme aiming to minimize the energy consumption, where the location privacy, usage pattern privacy and secure transmission against the eavesdropper are jointly considered. We formulate this problem as a constrained Markov decision process (CMDP) with the constraints of secure offloading rate and pre-specified privacy level, and solve it with reinforcement learning (RL). It can be concluded from the simulation that this scheme can save the energy consumption as well as improve the privacy level and security of the mobile device compared with the benchmark scheme.

With the development of wireless sensor networks (WSNs), WSNs have been widely used in various fields such as animal habitat detection, military surveillance, etc. This paper focuses on protecting the source location privacy (SLP) in WSNs. Existing algorithms perform poorly in non-uniform networks which are common in reality. In order to address the performance degradation problem of existing algorithms in non-uniform networks, this paper proposes a robust fixed path-based random routing scheme (RFRR), which guarantees the path diversity with certainty in non-uniform networks. In RFRR, the data packets are sent by selecting a routing path that is highly differentiated from each other, which effectively protects SLP and resists the backtracking attack. The experimental results show that RFRR increases the difficulty of the backtracking attack while safekeeping the balance between security and energy consumption.

Wireless Sensor Networks (WSNs) and their implementations have been the subject of numerous studies over the last two decades. WSN gathers, processes, and distributes wireless data to the database storage center. This study aims to explain the four main components of sensor nodes and the mechanism of WSN's. WSNs have 5 available types that will be discussed and explained in this paper. In addition to that, shortest path routing will be thoroughly analyzed. In “The Protocol”. Reconfigurable logic applications have grown in number and complexity. Shortest path routing is a method of finding paths through a network with the least distance or other cost metric. The efficiency of the shortest path protocol mechanism and the reliability of encryption are both present which adds security and accuracy of location privacy and message delivery. There are different forms of key management, such as symmetric and asymmetric encryption, each with its own set of processing techniques. The use of encryption technique to secure sensor nodes is addressed, as well as how we overcame the problem with the aid of advanced techniques. Our major findings are that adding more security doesn't cost much and by cost we mean energy consumption, throughput and latency.

In recent years, with the rise of the Internet of things industry, a variety of user location-based applications came into being. While users enjoy these convenient services, their location information privacy is also facing a great threat. Therefore, the research on location privacy protection in the Internet of things has become a hot spot for scholars. Privacy protection microdata publishing is a hot spot in data privacy protection research. Data interference is an effective solution for privacy protection microdata publishing. Aiming at privacy protection clustering problem, a privacy protection data interference method is proposed. In this paper, the location privacy protection algorithm is studied, with the purpose of providing location services and protecting the data interference of users' location privacy. In this paper, the source location privacy protection protocol (PR \_ CECRP) algorithm with controllable energy consumption is proposed to control the energy consumption of phantom routing strategy. In the routing process from the source node to the phantom node, the source data packet forwarding mechanism based on sector area division is adopted, so that the random routing path is generated and the routing energy consumption and transmission delay are effectively controlled.

Wireless Sensor Networks (WSNs) have limited energy resource which requires authentic data transmission at a minimum cost. The major challenge is to deploy WSN with limited energy and lifetime of nodes while taking care of secure data communication. The transmission of data from the wireless channels may cause many losses such as fading, noise, bit error rate increases as well as deplete the energy resource from the nodes. To reduce the adverse effects of losses and to save power usage, error control coding (ECC) techniques are widely used and it also brings coding gain. Since WSN have limited energy resource so the selection of ECC is very difficult as both power consumption, as well as BER, has also taken into consideration. This research paper reviews different types of models, their applications, limitations of the sensor networks, and what are different types of future works going to overcome the limitations.

With the increasing application of micro-nano satellite network, it is extremely vulnerable to the influence of internal malicious nodes in the practical application process. However, currently micro-nano satellite network still lacks effective means of routing security protection. In order to solve this problem, combining with the characteristics of limited energy and computing capacity of micro-nano satellite nodes, this research proposes a secure routing algorithm based on trust value. First, the trust value of the computing node is synthesized, and then the routing path is generated by combining the trust value of the node with the AODV routing algorithm. Simulation results show that the proposed MNS-AODV routing algorithm can effectively resist the influence of internal malicious nodes on data transmission, and it can reduce the packet loss rate and average energy consumption.

Storing information in Blockchain has become in vogue in the Technical and Communication Industry with many major players jumping into the bandwagon. Two of the most prominent enablers for Blockchain are “Proof of Work” and “Proof of Stake”. Proof of work includes the members solving the complex problem without having a particular need for the solution (except as evidence, of course), which absorbs a large number of resources in turn. The proof of stake doesn’t require as many resources to enable Blockchain secure information store. Both methodologies have their advantages and their shortcomings. The article attempts to review the current literature and collate the results of the study to measure the performance of both the methodologies and to arrive at a consensus regarding either or both methodologies to implement Blockchain to store data. Post reviewing the performance aspects and security features of both Proofs of Stake and Proof of Work the reviewer attempts to arrive at a secure and better performing blended Blockchain methodology that has wide industry practical application.

Wireless Sensor Network consists of hundreds to thousands of tiny sensor nodes deployed in the large field of the target phenomenon. Sensor nodes have advantages for its size, multifunctional, and inexpensive features; unfortunately, the resources are limited in terms of memory, computational, and in energy, especially. Network transmission between nodes and base station (BS) needs to be carefully designed to prolong the network life cycle. As the data transmission is energy consuming compared to data processing, designing sensor nodes into hierarchical network architecture is preferable because it can limit the network transmission. LEACH is one of the hierarchical network protocols known for simple and energy saving protocols. There are lots of modification made since LEACH was introduced for more energy efficient purposed. In this paper, hybridization of LEACH-C and LEACH-R and the modification have been presented for a more energy saving LEACH called LEACH-CR. Experimental result was compared with previous LEACH variant and showed to has advantages over the existing LEACH protocols in terms of energy consumption, dead/alive nodes, and the packet sent to Base Station. The result reflects that the consideration made for residual energy to select the cluster head and proximity transmission lead to a better energy consumption in the network.