This research aims to compare the performance between the Gated Recurrent Unit (GRU) and Long Short-Term Memory (LSTM) methods in predicting the Bitcoin exchange rate against the US Dollar (BTC-USD). The data used comes from Yahoo Finance for the period 2017-2022. Each model is built with a comparable architecture and evaluated using Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), coefficient of determination (R²), and prediction accuracy metrics. The results show that the LSTM model performed better on the test data with a MAPE of 3.80% and an accuracy of 96.20%, while the GRU model achieved a MAPE of 5.13% and an accuracy of 94.87%. Although the GRU model performed better on the training data, the LSTM model showed better generalization ability on the testing data. This research provides important insights into the selection of the optimal recurrent neural network architecture for Bitcoin exchange rate prediction which is known for its high volatility.
It is the expansion and use of the Industrial Internet of Things (IIoT) in various industrial sectors and applications that are referred to as the Industrial Internet of Things (IIoT). The Industrial Internet of Things includes industrial applications such as robots, medical devices, and softwareâdefined manufacturing processes. In terms of energy conservation, routing is extremely essential. The creation of an energy effectual steering procedure leads to a substantial rise in energy consumption. To minimize network traffic and increase network life, the article presented an Industrial IoT Fuzzy Logic EnergyâAware Routing Protocol (FLEAâRPL), which decreases network traffic as well as improves network life. The most suitable parent for data transfer is selected based on, among other things, the routing parameters charge, residual energy, and expected transmission count. Since the load routing metric is taken into consideration during the construction of the route, the data traffic is spread across the network. This increases networkâs lifetime while maintaining a high packet delivery ratio. The proposed work proposes a Multilayer EnergyâAware Aware RPL (MCEAâRPL) cluster for the Internet of Things to decrease network data traffic while increasing the lifetime of the network. It is split into three phases, each including the creation of network rings, intraring divisions, and intercluster routing. First and foremost, the virtual ring is created in the network. Secondly, each ring forms an identical cluster and chooses the CH node. Finally, it is responsible for the maintenance and performance of the DODAG. Data transfer from the lesser sheet to the DODAG root is known as data transfer. By using Blockchain technology, the lifetime of a network may be extended by reducing the number of identical data package transfers. This article offers Enhanced Mobility Support RPL (EMâRPL) in Industrial IoT which enhances mobility support with blockchain and spreads system generation. It comprises two processes: a collection of the parental static node and selection of the parent moving node. The static parent selection method uses routing metrics load and residual energy to identify the parent that is most suited for data transfer. Two phases of mobile parent selection must be distinguished: data transmission and route rediscovery. The mobile node utilizes furious logic to compute the handâoff value of the metric packet errors ratio and the signal strength indication received from the base station. If the handâoff value exceeds the threshold limit, the DODAG route has to be changed to work correctly. The EMâRPL thus increases the package delivery rate by reducing the amount of route interruption caused by mobility, while offering an efficient handling mechanism.
Adeel Ahmed, Saima Abdullah, Muhammad Bukhsh, Israr Ahmad ¡ 5 authors
The Internet of Things (IoT) is getting important and interconnected technologies of the world, consisting of sensor devices. The internet is smoothly changing from an internet of people towards an Internet of Things, which permits various objects to connect to another wirelessly. The energy consumption of the IoT routing protocol can affect the network life span. In addition, the high volume of data produced by IoT will result in transmission collision, security issues, and energy dissipation due to increased data redundancy because tiny sensors are usually hard to recharge after they are deployed. Generally, to save energy, data aggregation reduces data redundancy at each node by turning some nodes into sleep mode and others into wake mode. Therefore, it is important to group the nodes with high data similarity using the fuzzy matrix. Then, the data received from the member nodes at the Cluster Head (CH) are analyzed using a fuzzy similarity matrix for clustering. In the next step, after clustering, some nodes are chosen from all groups as redundant nodes. The sleep scheduling mechanism is then applied to reduce data redundancy, network traffic jamming, and transmission costs. We have proposed an Energy-Efficient Data Aggregation Mechanism (EEDAM) secured by blockchain, which uses a data aggregation mechanism at the cluster level to save energy. As edge computing is used to provide on-demand trusted services to IoT with minimum delay, blockchain is integrated inside a cloud server, so the edge is validated by the blockchain to provide secure services to IoT. Finally, we performed simulations to calculate the performance of the proposed mechanism and compared it with the conventional energy-efficient algorithms. The simulation results show that the proposed structural design can successfully reduce the amount of data, provide proper security to the IoT, and extend the wireless sensor network (WSN).
Wireless sensor networks (WSNs) and Internet of Things (IoT) have gained more popularity in recent years as an underlying infrastructure for connected devices and sensors in smart cities. The data generated from these sensors are used by smart cities to strengthen their infrastructure, utilities, and public services. WSNs are suitable for long periods of data acquisition in smart cities. To make the networks of smart cities more reliable for sensitive information, the blockchain mechanism has been proposed. The key issues and challenges of WSNs in smart cities is efficiently scheduling the resources; leading to extending the network lifetime of sensors. In this paper, a linear network coding (LNC) for WSNs with blockchain-enabled IoT devices has been proposed. The consumption of energy is reduced for each node by applying LNC. The efficiency and the reliability of the proposed model are evaluated and compared to those of the existing models. Results from the simulation demonstrate that the proposed model increases the efficiency in terms of the number of live nodes, packet delivery ratio, throughput, and the optimized residual energy compared to other current techniques.
Eryk Schiller, Elfat Esati, Sina Rafati Niya, Burkhard Stiller
This work develops an integration of Blockchains (BC) with the Internet-of-Things (IoT) using a highly constrained TelosB IoT platform based on the MSP430 processor family and CC2420 IEEE 802.15.4-compliant radio interfaces. The system is evaluated in an indoor office environment focusing on overhead and energy efficiency of BC transaction (TX) transmissions.
Speech is a natural user interface for the Internet of Things system. However, the presence of noise affects severely the performance of such system. With the deployment of smart devices with microphones, one can form a powerful acoustic sensor network to enhance the speech via beamforming techniques. On the other hand, reliability of data transmission also determines the beamforming performance, since faulty data will drift the beamformer steering location randomly. Currently, there is no protection scheme for acoustic data transmitted over the wireless network in order to keep steady beamforming performance. In this article, we design a compound distributed beamformer, where nodes are grouped and the system is embedded with blockchain technology to protect the data integrity during transmission. It attempts to provide more possible reliable connections between groups. Simulated experiments show that the distributed beamformer with blockchain protection is able to maintain steady beamforming performance.
PaweĹ ĹniataĹa, M. Hadi Amini, Kianoosh G. Boroojeni
Information fusion has been a topic of immense interest owing to its applicability in various applications. This brings to the fore the need for a flexible and accurate fusion algorithm that can be versatile. The BrooksâIyengar algorithm is one such fusion algorithm. It has since its inception found numerous applications that deal with the fusion of data from multiple sources. The uniqueness of the BrooksâIyengar algorithm is the ease with which the data from multiple sensors in a local system can be fused and also reach consensus in a distributed system with the added capability of fault tolerance. Blockchain has found its use as a distributed ledger and has successfully supported and fueled many crypto-currencies over the years. Information fusion with regard to blockchains is a topic of great research interest in the past couple of years. Since blockchain has no official node, the introduction of a decentralized network and a consensus algorithm is required in making the interactions and exchanges between multiple suppliers easier and thus leads to business being carried out without any hassles. In this paper, we attempt to understand and describe the deployment of multiple sensors to measure various aspects of the physical world. We discuss a novel technique of employing the BrooksâIyengar algorithm in the design of the system that would decentralize the data source from the corresponding measurements and thus ensure the integrity of the transactions in the blockchain. Finally, a theoretical analysis of the performance of the algorithm when used in a blockchain based decentralized environment is also discussed.
Open access
Distributed systems and fault tolerance
Distributed Sensor Networks and Detection Algorithms
A trusted routing scheme is very important to ensure the routing security and efficiency of wireless sensor networks (WSNs). There are a lot of studies on improving the trustworthiness between routing nodes, using cryptographic systems, trust management, or centralized routing decisions, etc. However, most of the routing schemes are difficult to achieve in actual situations as it is difficult to dynamically identify the untrusted behaviors of routing nodes. Meanwhile, there is still no effective way to prevent malicious node attacks. In view of these problems, this paper proposes a trusted routing scheme using blockchain and reinforcement learning to improve the routing security and efficiency for WSNs. The feasible routing scheme is given for obtaining routing information of routing nodes on the blockchain, which makes the routing information traceable and impossible to tamper with. The reinforcement learning model is used to help routing nodes dynamically select more trusted and efficient routing links. From the experimental results, we can find that even in the routing environment with 50% malicious nodes, our routing scheme still has a good delay performance compared with other routing algorithms. The performance indicators such as energy consumption and throughput also show that our scheme is feasible and effective.
The Internet of Things (IoT) has been widely used because of its high efficiency and real-time collaboration. A wireless sensor network is the core technology to support the operation of the IoT, and the security problem is becoming more and more serious. Aiming at the problem that the existing malicious node detection methods in wireless sensor networks cannot be guaranteed by fairness and traceability of detection process, we present a blockchain trust model (BTM) for malicious node detection in wireless sensor networks. First, it gives the whole framework of the trust model. Then, it constructs the blockchain data structure which is used to detect malicious nodes. Finally, it realizes the detection of malicious nodes in 3D space by using the blockchain smart contract and the WSNs' quadrilateral measurement localization method, and the voting consensus results are recorded in the blockchain distributed. The simulation results show that the model can effectively detect malicious nodes in WSNs, and it can also ensure the traceability of the detection process.
Ubiquitous sensing enabled by Wireless Sensor Network (WSN) technologies cuts across many areas of modern day living. This offers the ability to measure, infer and understand environmental indicators, from delicate ecologies and natural resources to urban environments. The proliferation of these devices in a communicatingâactuating network creates the Internet of Things (IoT), wherein sensors and actuators blend seamlessly with the environment around us, and the information is shared across platforms in order to develop a common operating picture (COP). Fueled by the recent adaptation of a variety of enabling wireless technologies such as RFID tags and embedded sensor and actuator nodes, the IoT has stepped out of its infancy and is the next revolutionary technology in transforming the Internet into a fully integrated Future Internet. As we move from www (static pagesâ web) to web2 (social networking web) to web3 (ubiquitous computing web), the need for data-on-demand using sophisticated intuitive queries increases significantly. This paper presents a Cloud centric vision for worldwide implementation of Internet of Things. The key enabling technologies and application domains that are likely to drive IoT research in the near future are discussed.
Sep 1, 2017¡2017 9th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS)
In the article the features of wireless smart biosensor for sensor networks in ecological monitoring are considered. The developed wireless smart sensors manufactured on certified contract manufacture, is described. The results of wireless sensors testing are given.
Ubiquitous sensing enabled by Wireless Sensor Network (WSN) technologies cuts across many areas of modern day living. This offers the ability to measure, infer and understand environmental indicators, from delicate ecologies and natural resources to urban environments. The proliferation of these devices in a communicating-actuating network creates the Internet of Things (IoT), wherein, sensors and actuators blend seamlessly with the environment around us, and the information is shared across platforms in order to develop a common operating picture (COP). Fuelled by the recent adaptation of a variety of enabling wireless technologies such as RFID tags and embedded sensor and actuator nodes, the IoT has stepped out of its infancy and is the the next revolutionary technology in transforming the Internet into a fully integrated Future Internet. As we move from www (static pages web) to web2 (social networking web) to web3 (ubiquitous computing web), the need for data-on-demand using sophisticated intuitive queries increases significantly. This paper presents a Cloud centric vision for worldwide implementation of Internet of Things. The key enabling technologies and application domains that are likely to drive IoT research in the near future are discussed. A Cloud implementation using Aneka, which is based on interaction of private and public Clouds is presented. We conclude our IoT vision by expanding on the need for convergence of WSN, the Internet and distributed computing directed at technological research community.
Purpose This paper aims to present an approach for a bioâinspired decentralization topology control mechanism, called forceâbased genetic algorithm (FGA), where a genetic algorithm (GA) is run by each holonomic autonomous vehicle (HAV) in a mobile ad hoc network (MANET) as software agent to achieve a uniform spread of HAVs and to provide a fully connected network over an unknown geographical terrain. An HAV runs its own FGA to decide its next movement direction and speed based on local neighborhood information, such as obstacles and the number of neighbors, without a centralized control unit or global knowledge. Design/methodology/approach The objective function used in FGA is inspired by the equilibrium of the molecules in physics where each molecule tries to be in the balanced position to spend minimum energy to maintain its position. In this approach, a virtual force is assumed to be applied by the neighboring HAVs to a given HAV. At equilibrium, the aggregate virtual force applied to an HAV by its neighbors should sum up to zero. If the aggregate virtual force is not zero, it is used as a fitness value for the HAV. The value of this virtual force depends on the number of neighbors within the communication range of R com and the distance among them. Each chromosome in our GAâbased framework is composed of speed and movement direction. The FGA is independently run by each HAV as a topology control mechanism and only utilizes information from neighbors and local terrain to make movement and speed decisions to converge towards a uniform distribution of HAVs. The authors developed an analytical model, simulation software and several testbeds to study the convergence properties of the FGA. Findings The paper finds that coverageâcentric, bioâinspired, mobile node deployment algorithm ensures effective sensing coverage for each mobile node after initial deployment. The FGA is also an energyâaware selfâorganization framework since it reduces energy consumption by eliminating unnecessary excessive movements. Faultâtolerance is another important feature of the GAâbased approach since the FGA is resilient to losses and malfunctions of HAVs. Furthermore, the analytical results show that the authors' bioâinspired approach is effective in terms of convergence speed and area coverage uniformity. As seen from the experimental results, the FGA delivers promising results for uniform autonomous mobile node distribution over an unknown geographical terrain. Originality/value The proposed decentralized and bioâinspired approach for autonomous mobile nodes can be used as a realâtime topology control mechanism for commercial and military applications since it adapts to local environment rapidly but does not require global network knowledge.
Based on distributed key management, the trust mechanism of the local group, zero-knowledge proof and timestamp exchange mechanism of OLSR routing protocol, a new lightweight and robust group-key management protocol for Ad Hoc Networks is proposed. the protocol achives the advantages of low energy consumption, high efficiency and high availability by reducing both the number of control messages and the energy spent with cryptographic operations. Then, we demonstrates the security and anti-offensive of the protocol through safety analysis. Finally, simulation results show that the proposed protocol can ensure the security of Ad Hoc networks with a very low-impact on the performance of secure routing protocol.
Ubiquitous sensing enabled by Wireless Sensor Network (WSN) technologies cuts across many areas of modern day living. This offers the ability to measure, infer and understand environmental indicators, from delicate ecologies and natural resources to urban environments. The proliferation of these devices in a communicating-actuating network creates the Internet of Things (IoT), wherein, sensors and actuators blend seamlessly with the environment around us, and the information is shared across platforms in order to develop a common operating picture (COP). Fuelled by the recent adaptation of a variety of enabling device technologies such as RFID tags and readers, near field communication (NFC) devices and embedded sensor and actuator nodes, the IoT has stepped out of its infancy and is the the next revolutionary technology in transforming the Internet into a fully integrated Future Internet. As we move from www (static pages web) to web2 (social networking web) to web3 (ubiquitous computing web), the need for data-on-demand using sophisticated intuitive queries increases significantly. This paper presents a cloud centric vision for worldwide implementation of Internet of Things. The key enabling technologies and application domains that are likely to drive IoT research in the near future are discussed. A cloud implementation using Aneka, which is based on interaction of private and public clouds is presented. We conclude our IoT vision by expanding on the need for convergence of WSN, the Internet and distributed computing directed at technological research community.
Lalitha Bhavani Jivanadham, A. K. M. Muzahidul Islam, Nafees Mansoor, Sabariah Baharun
Driven by the technology advances in Micro-Electro-Mechanical Systems which has facilitated the development of smart sensors; we have witnessed in recent years the emergence of WSNs in environment, military, surveillance, natural disaster relief, healthcare, etc. These WSNs carry the promise of drastically improving and expanding the quality of services across a wide variety of settings and for different segments of the population. Therefore, it is very important to develop a WSN with robustness and security in mind. Typically, the sensors are smaller in sizes that have limited processing and computational power resources, and are inexpensive, thus enabling the network to have a large coverage area and longer range. By using hundreds or thousands of them it is possible to build a high quality, fault-tolerant sensor network where failure of one or few nodes does not affect the operation of the network. They are also self-configuring or self-organizing. In this paper, we propose formation of a Secured Cluster-based architecture for a Dynamic Wireless Sensor Network that uses two topology management operations: node-move-in and node-move-out. The proposed security protocol integrates one round Zero Knowledge Proof and AES algorithm to apply for node authentication, where only authenticated nodes will be accepted during node-move-in operation. We also show that it requires O(h+q) rounds for a node to join into a network securely, where h is the height of the dynamic cluster-based wireless sensor network and q is the number of neighbouring nodes of a joining node.
Ubiquitous sensing enabled by Wireless Sensor Network (WSN) technologies cuts\nacross many areas of modern day living. This offers the ability to measure,\ninfer and understand environmental indicators, from delicate ecologies and\nnatural resources to urban environments. The proliferation of these devices in\na communicating-actuating network creates the Internet of Things (IoT),\nwherein, sensors and actuators blend seamlessly with the environment around us,\nand the information is shared across platforms in order to develop a common\noperating picture (COP). Fuelled by the recent adaptation of a variety of\nenabling device technologies such as RFID tags and readers, near field\ncommunication (NFC) devices and embedded sensor and actuator nodes, the IoT has\nstepped out of its infancy and is the the next revolutionary technology in\ntransforming the Internet into a fully integrated Future Internet. As we move\nfrom www (static pages web) to web2 (social networking web) to web3 (ubiquitous\ncomputing web), the need for data-on-demand using sophisticated intuitive\nqueries increases significantly. This paper presents a cloud centric vision for\nworldwide implementation of Internet of Things. The key enabling technologies\nand application domains that are likely to drive IoT research in the near\nfuture are discussed. A cloud implementation using Aneka, which is based on\ninteraction of private and public clouds is presented. We conclude our IoT\nvision by expanding on the need for convergence of WSN, the Internet and\ndistributed computing directed at technological research community.\n
The three critical resources in any cluster based Wireless Sensor Networks (WSN) are Sensor Nodes (SN), Cluster Head (CH) and Base Station (BS). The main job of Cluster Head is to send all the aggregated data collected from multiple sensor Nodes to the Base Station. But before sending the data, the Cluster Head must be perfectly sure about the actual identity of Base Station. If any intruder acts as Base Station and receives the aggregated data the entire security of the network is spoiled. To avoid this, the authenticity of the base station needs to be checked by cluster head before sending any data. Many asymmetric authentication techniques like digital signatures are difficult to implement in resource constraint sensor Nodes. A zero-knowledge proof protocol is a powerful cryptographic system that can be applied in many cryptographic applications and operations such as identification, authentication, key exchange and others, but many of the existing zero knowledge proof of identity involves exponential calculations which are not suitable for WSN. To overcome the limitations of the existing techniques, the proposed protocol provides Zero knowledge proof identity using congruence equations. Here, Triplet simultaneous linear congruence equations are used to subdivide a whole secret into three shares. A part of a secret is used as identity of the base station and stored in it. The Base Station won't reveal the part of secret stored in it to the Cluster Head but the Cluster Head confirms the true identity without getting the actual secret from Base Station. In this scheme, the Cluster Heads need to solve only congruence equations which need only limited computation and memory. When the proposed protocol is compared with existing protocols it shows that it provide instant authentication, with minimum memory utilization and withstand many attacks.
We address a self-organizing network problem for autonomous robotic sensor swarms towards various ad hoc sensor network applications. For this purpose, a decentralized solution approach is proposed to enable individual robots to build and to maintain their network through locally communicative interactions with adjacent robots. Specifically, the communicative interaction allows robots to select specific neighbors with higher connectivity, and to adapt to network topological changes by robot movements. The proposed approach is verified to be effective through extensive simulations for the secure self-organization of a robotic sensor network. Considering the realistic conditions of communication delays and re-configuration for robot disappearances due to robot failures, simulations are also performed.
Wireless Sensor Networks (WSNs) offer an excellent opportunity to monitor environments, and have a lot of interesting applications, some of which are quite sensitive in nature and require full proof secured environment. The security mechanisms used for wired networks cannot be directly used in sensor networks as there is no user-controlling of each individual node, wireless environment, and more importantly, scarce energy resources. In this paper, we address some of the special security threats and attacks in WSNs. We propose a scheme for detection of distributed sensor cloning attack and use of zero knowledge protocol (ZKP) for verifying the authenticity of the sender sensor nodes. The cloning attack is addressed by attaching a unique fingerprint to each node, that depends on the set of neighboring nodes and itself. The fingerprint is attached with every message a sensor node sends. The ZKP is used to ensure non transmission of crucial cryptographic information in the wireless network in order to avoid man-in-the middle (MITM) attack and replay attack. The paper presents a detailed analysis for various scenarios and also analyzes the performance and cryptographic strength.
In this paper, we address the problem of decentralized parameter estimation via hierarchical organizations of sensors. In this setup, the nodes are organized in clusters, and a sensor is designated as a cluster-head depending on its channel conditions. The task of the network is to estimate an unknown parameter with the minimum possible distortion, while ensuring a prescribed total power consumption. To this aim, we consider analog transmissions and, further, we decompose the problem into smaller subproblems, which can be autonomously solved for each cluster-head. We show that by balancing the total amount of power between the cluster-heads and the sensors, one can increase the estimation accuracy, and we derive a closed-form expression of the optimum balancing for the Uniform Power Allocation(UPA) case. Next, we propose some hybrid solutions which combine UPA with optimal WF schemes. Finally, we assess the performance of the proposed schemes by means of computer simulations, and we carry out a comparison with the non-hierarchical strategy as a baseline.
Distributed Sensor Networks and Detection Algorithms