Resource-constrained Wireless Sensor Networks Research Articles

BS-SCRM: a novel approach to secure wireless sensor networks via blockchain and swarm intelligence techniques

In this paper, we present a novel Secure Clustering Routing Method based on Blockchain and Swarm Intelligence (BS-SCRM) for Wireless Sensor Networks (WSNs), which serves as a cornerstone in the Internet of Things (IoT) infrastructure. Recognizing the limitations of existing clustering routing methods in addressing security threats, our approach integrates blockchain technology to fortify WSNs against vulnerabilities such as man-in-the-middle attacks. The proposed BS-SCRM method is structured in two phases: (1) an enhanced cluster head (CH) election utilizing an elite strategy-enhanced Whale Optimization Algorithm (WOA) that considers node energy and proximity to the base station, and (2) a secure data on-chain phase where blockchain comes into play, encrypting and validating cluster data to safeguard integrity and prevent tampering. We further tackle the challenge of implementing blockchain in resource-constrained WSNs by assigning distinct roles to devices, i.e., ordinary nodes with data viewing permissions and accounting nodes entrusted with both data viewing and consensus algorithm execution. Extensive simulations confirm that BS-SCRM not only improves clustering quality but also provides a more secure and energy-efficient routing solution compared to contemporary methods. More specifically, simulation results in different scenarios demonstrate that BS-SCRM enhances network lifetime by 24–73% compared to other clustering methods when facing attacks.

Enhancement of Tiny Encryption Algorithm for Resource-Constrained WSNs Using Four Connected Additive Fibonacci Generators

The usage of wireless sensor networks (WSN) is widespread in industries where data security is crucial. Due to the energy and computational limits of WSN, the cryptographic protocols designed for it must be as computationally cheap as feasible. The components of these protocols, such as the random number generator, are subject to the same constraints. For such resource-constrained devices, several lightweight encryption techniques have been created. One of the most efficient lightweight ciphers is Tiny Encryption Algorithm (TEA). TEA uses a few lines of source code that are based on Feistel. It is however susceptible to attacks utilizing equivalent and related key attacks. In order to address TEA’s key vulnerabilities, a modification is suggested in this paper that focuses on key creation. Four connected Additive Fibonacci Generators (AFGs) make up the structure, which addresses the security vulnerability by using a unique key each round. Performance evaluation was assessed using three statistical tests: avalanche effect, randomness analysis, and completeness testing. Through experimental results, ATEA outperforms TEA by an average of 51.68 % to 47.51 % for the avalanche effect, and 51.95 % to 48.36 % for the completeness test, and satisfies all the NIST requirements. Results of advanced security measurements indicate that, ATEA can be used to secure WSN devices.

Global probability distribution structure-sparsity filter pruning for edge fault diagnosis in resource constrained wireless sensor networks

In this paper, a global probability distribution structure-sparsity filter pruning is proposed to address the problem of difficult deployment of diagnostic models in resource constrained wireless sensor networks (WSNs) for edge fault diagnosis. Firstly, local and global weight distributions are analyzed. A global probability distribution weight sparsity method is proposed to obtain the global sparse boundary and the important features of the model. Secondly, according to the distribution of weight amplitude, the problem of uneven weight distribution is disclosed. A computational acceleration sensing method is proposed to reduce the high computational cost caused by small weight amplitude with low importance estimation. Moreover, a novel layer filter clustering pruning scheme is proposed. The intra-layer filters are classified and redundant filters are removed by using the clustering idea according to the predetermined pruning rate. Thus, the structure-sparsity filter pruning is realized. Compared with other advanced filter pruning methods, the proposed method analyzes the global and local model weight distribution and the influence of weight importance on the computational complexity of the model. The proposed method then performs filter pruning on the importance of overall filters for each network layer rather than individual filters. The accuracy of the proposed method on experimental dataset for pruned ResNet8 model under pruning rate at 0.95 is 99.12% with 3.668K parameters and 3.636MFLOPs. The experimental results elucidate that the proposed method is more suitable for deployment in resource-constrained WSNs for fault diagnosis of rotating machinery. This provides a potential solution for practical engineering applications.

ECC-based three-factor authentication and key agreement scheme for wireless sensor networks

In wireless sensor networks (WSNs), protocols with authentication and key agreement functions can enhance the security of the interaction between users and sensor nodes, guaranteeing the security of user access and sensor node information. Existing schemes have various security vulnerabilities and are susceptible to security attacks (e.g., masquerading user, password guessing, internal privilege, and MITT attacks), so they cannot meet the anonymity requirements or achieve forward security. To effectively improve the security performance of WSNs, an elliptic curve cryptography (ECC)-based three-factor authentication and key agreement scheme for WSNs is proposed. The scheme is based on the ECC protocol and combines biometrics, smart card and password authentication technology; uses a challenge/response mechanism to complete the authentication between users, gateways, and sensors; and negotiates a secure session key. The Burrows, Abadi and Needham logic for formal security analysis proves the correctness and security of the scheme, and the informal analysis of multiple known attacks proves that the scheme can resist various attacks and has high security characteristics. The feasibility of the scheme has been analysed and verified with the ProVerif tool. The efficiency analysis results show that the scheme is suitable for resource-constrained WSNs.

Efficient lightweight cryptography for resource-constrained WSN nodes

In Wireless Sensor Networks (WSN), where resources are scarce, lightweight cryptography is crucial and difficult. The Constrained Application Protocol (CoAP), Field-Programmable Gate Array (FPGA) technology, and a modified RC5 algorithm are used in this study to achieve efficient encryption for WSN nodes. WSN are essential for monitoring temperature, humidity, vibrations, and seismic activity. However, processing capacity, memory, and energy constraints make data security difficult in these networks. CoAP, FPGA, and a modified RC5 algorithm provide security, computational load reduction, and energy efficiency in resource-limited WSN. This research is crucial because it creates cryptographic mechanisms that accommodate WSN unique needs. By protecting data integrity and confidentiality, these mechanisms ensure data transmission security and privacy. Data security and efficiency in WSN require innovative cryptographic, FPGA-based hardware acceleration, and protocol optimization solutions. These methods could improve these vital sensing networks. This research lays the foundation for protecting the accuracy and security of WSN node data collected and transmitted with limited resources while optimizing resource allocation.

Metallic Sensor: Architecture, Protocols, and Functions Of Wireless Network

Metals such as silver, gold, platinum, palladium, and rhodium are widely used in automotive sensors and communication devices Sensor frameworks are comprehensively conveyed in a collection of purposes going from military to environmental and helpful examination. In various applications, for instance, target following, disaster area perception and gate crasher area, metallic sensor network habitually work in unfriendly and unattended circumstances. Subsequently, there is a strong prerequisite for getting the distinguishing data and identifying readings. In remote circumstances, an enemy can see the radio traffic too as can catch or between rupt the exchanged messages. Thusly, various shows and estimations don't simply work in that frame of mind without having palatable wellbeing endeavours. Consequently, security winds up one of the critical worries while illustrating security shows in resource constrained wireless sensor networks (WsNs). The ebb and flow paper are a fair endeavour to draw in the consideration of the peruses towards the design, conventions and elements of remote sensor network on the grounds that comprehensively conveyed in a variety of purposes going from military to biological and helpful examination. The present paper is an honest attempt to attract the attention of the readers towards the architecture, protocols and functions of metallic wireless sensor network because it is broadly conveyed in an assortment of uses going from military to ecological and restorative research.

Energy Saving Routing Algorithm for Wireless Sensor Networks Based on Minimum Spanning Hyper Tree

With the rapid development of wireless sensor networks (WSNs), designing energy-efficient routing protocols has become essential to prolong network lifetime. This paper proposes a minimum spanning tree-based energy-saving routing algorithm for WSNs. First, sensor nodes are clustered using the LEACH protocol and minimum spanning trees are constructed within clusters and between cluster heads. The spanning tree edge weights are optimized considering transmission energy, residual energy, and energy consumption rate. This avoids channel competition and improves transmission efficiency. An energy-saving routing model is then built whereby deep reinforcement learning (DRL) agents calculate paths optimizing the energy utilization rate. The DRL reward function integrates network performance metrics like energy consumption, delay, and packet loss. Experiments show the proposed approach leads to 10-15W lower average switch energy consumption compared to existing methods. The throughput is high since overloaded shortest paths are avoided. The average path length is close to shortest path algorithms while maintaining energy efficiency. In summary, the proposed minimum spanning tree-based routing algorithm successfully achieves energy-saving goals for WSNs while guaranteeing network performance. It provides an efficient and adaptive routing solution for resource-constrained WSNs.

A secure and effective data aggregation in WSN for improved security and data privacy

The increasing prevalence of internet usage and mobile devices has underscored the critical importance of safeguarding personal data. This is especially important in Wireless Sensor Networks (WSNs), where information typically requires in-network computing and collaborative processing. These computationally demanding approaches are not suitable for resource-constrained WSN nodes. Aggregating data effectively while protecting user data is a major challenge in wireless sensor networks. Many privacies of homomorphism encryption-based WSN data aggregation methods have been created and investigated recently. Since cluster leaders (aggregators) may rapidly combine cypher texts without decryption, communication overhead is reduced, making these data aggregation methods more secure than traditional ones. However, the base station only receives aggregated output, causing issues. Initial limits apply to aggregating functions. If the aggregated output is the sum of sensing data, the base station cannot acquire the maximum value. Second, attaching message digests or signatures to sensory samples does not allow the base station to validate data authenticity. In dangerous places, WSNs must be energy-efficient and private. In this research, we present a data aggregation method known as Energy-Efficient and Privacy-Preserving (E2P2). E2P2 data aggregation utilizes less energy and yields more accurate results. Private data aggregation with increased accuracy and hybrid encryption is presented in this research. The goal is to reduce data transmission and energy use collisions and offset collision-induced loss. Extensive simulations compare E2P2 to earlier approaches. Experimental results show that E2P2 outperforms other algorithms. Good exactness, complexity, and safety are demonstrated by theoretical and simulation results.

An improved authentication and key management scheme in context of IoT-based wireless sensor network using ECC

Wireless sensor network (WSN) is used for sensing data/information from the nearby environment with the help of various spatially dispersed miniature sensor devices/nodes and forwarding the data to the direction of the base station which is connected to the network through the Internet for processing the collected data. Currently, Internet of Things (IoT) based smart computing applications are becoming popular in which WSN is used to run a process to output information from sensed data or events. However, these sensor nodes have less computation ability and low power capacity. In these resource-constrained WSNs, remote user authentication is a serious challenge that demands the attention of the researchers’ community. Several authentication protocols which address a variety of security concerns are present in the literature. Ghani et al. proposed an efficient authentication and key management scheme that claims to be resistant to known attacks. During the study of their scheme, we found that it is efficient and effective for protecting against a few attacks. However, it does not have the ability to counter several security threats like user impersonation attack, insider attack, database attack, and stolen smart card attack. To counter these aforesaid security loopholes, in this paper a new ECC integrated scheme suitable for IoT-based WSN with a higher level of protection is proposed. The informal security analysis using mathematical approaches and the formal security analysis using a well-accepted AVISPA simulator and BAN logic ensures that the suggested work is highly secure from all the relevant threats. Moreover, the performance analysis and comparison of the proposed scheme with the most recent relevant schemes proves that our scheme is much more efficient in terms of computation, communication, and storage overheads than all the available schemes in the literature.

A Delay-Tolerant low-duty cycle scheme in wireless sensor networks for IoT applications

Energy efficiency is an existing research challenge in wireless sensor networks (WSNs); to make WSNs energy efficient, many researchers moved to low-duty cycle WSNs with different data forwarding schemes. Low-duty cycle is considered to be a promising approach to design routing protocols for resource-constrained WSNs. Many applications have real-time constraints, which requires an event must be reported to the sink before deadline. Furthermore, wireless links between low-power radios are highly unreliable, and end-to-end latency requirements are challenging due to multiple transmissions for a single packet delivery. By considering probabilistic delay (i.e., delay bounded data delivery with reliability constraints) and energy efficiency issues, little research has been done using different data forwarding schemes, but still, the problem exists. In this work, two strategies are proposed for data forwarding: an energy-optimal path and a delay-optimal path. The early-arrived packet follows an optimal energy route and a diverse path to achieve a delay guarantee with minimum transmission cost. The proposed scheme improves the performance of low-duty cycle WSNs in terms of delay, reliability, stability, and transmission cost. It is observed that under different low-duty cycles, the proposed scheme achieves up to 25% energy conservation compared with both MinEEC and MinEED. At last, undirected spanning trees are considered to balance communication costs. The simulation results of the proposed scheme are compared with the existing methods.

Energy-Efficient Object Detection and Tracking Framework for Wireless Sensor Network

Object detection and tracking is one of the key applications of wireless sensor networks (WSNs). The key issues associated with this application include network lifetime, object detection and localization accuracy. To ensure the high quality of the service, there should be a trade-off between energy efficiency and detection accuracy, which is challenging in a resource-constrained WSN. Most researchers have enhanced the application lifetime while achieving target detection accuracy at the cost of high node density. They neither considered the system cost nor the object localization accuracy. Some researchers focused on object detection accuracy while achieving energy efficiency by limiting the detection to a predefined target trajectory. In particular, some researchers only focused on node clustering and node scheduling for energy efficiency. In this study, we proposed a mobile object detection and tracking framework named the Energy Efficient Object Detection and Tracking Framework (EEODTF) for heterogeneous WSNs, which minimizes energy consumption during tracking while not affecting the object detection and localization accuracy. It focuses on achieving energy efficiency via node optimization, mobile node trajectory optimization, node clustering, data reporting optimization and detection optimization. We compared the performance of the EEODTF with the Energy Efficient Tracking and Localization of Object (EETLO) model and the Particle-Swarm-Optimization-based Energy Efficient Target Tracking Model (PSOEETTM). It was found that the EEODTF is more energy efficient than the EETLO and PSOEETTM models.

Energy – Efficient routing and scheduling using clustering in geographic routing protocol

In the resource-constrained wireless sensor network (WSN) geographic routing has been considered as an attractive method where it exploits the location data instead of global topology to transmit the data. The geographic routing protocol faces the routing issues when it is used by a heterogeneous device and utilizes high energy during the propagation of data. The lifespan of the sensor network depends on the efficiency of energy and capacity of the battery. Hence, successful data transmission, enrichment of battery capacity and energy utilization is necessary for WSN. To attain this requirements an effective change is made in the data transmission environment and network topology. In this paper proposed a dynamic cluster based duty cycle scheduling is initiated for the data transmission. The cluster-based scheduling and routing in geographic routing protocol (CSRGR) utilize the clustering mechanism which in turn reduces the consumption of energy and maximizes the throughput. The objective function of the proposed approach provides a scheduling and routing strategy. The demonstration of simulation results shows the effective cluster size balancing with data transmission range dynamically. The proposed algorithm is compared with the existing approach and from the results, the energy consumption is minimum for diverse scenarios.

A Timestamp-Free Time Synchronization Scheme Based on Reverse Asymmetric Framework for Practical Resource-Constrained Wireless Sensor Networks

Energy-efficient time synchronizations for wireless sensor networks (WSNs) have been put under the spotlight for years. A promising technique among which is the timestamp-free approach where no timestamps are required to establish the synchronization, thereby sparing the transmissions of the timing messages for conserving significant transmission energy. In this paper, we first investigate the feasibility of adopting timestamp-free time synchronization in practical resource-constrained WSNs; we then identify the issue of inaccuracy in maintaining the pre-defined response interval which affects the foundations of most existing timestamp-free schemes. Based on the investigation and our previously proposed reverse asymmetric time synchronization framework, we further propose an asymmetric timestamp-free time synchronization scheme with two estimation methods tailored for resource-constrained WSNs. We as well introduce the centralized and distributed multi-hop extension methods for the proposed scheme to cover diverse multi-hop scenarios. Experimental results on a real WSN testbed consisting of TelosB motes running TinyOS demonstrate that the proposed scheme achieves high energy efficiency while maintaining microsecond-level time synchronization accuracy compared to three other conventional schemes.

Reachability problems in interval-constrained and cardinality-constrained graphs

In this paper, we present two new graph structures that can be used to model information routing in heterogeneous communication networks. These are cardinality-constrained graphs (CCGs) and interval-constrained graphs (ICGs). In both cardinality- and interval-constrained graphs, the nodes in the graph are labeled. In a CCG, each label is associated with an upper bound. A path in a CCG is considered valid, if the number of nodes on the path with any particular label is at most the upper bound associated with that label. In an ICG, each label is associated with both an upper bound and a lower bound. A path in an ICG is considered valid, if the number of nodes on the path with any particular label is between the upper and lower bounds associated with that label. In this paper, we examine the computational complexities of finding valid paths in both CCGs and ICGs. In particular, we show that these problems are NP-hard. Additionally, we propose novel algorithms for finding paths in CCGs and a subset of ICGs. We also demonstrate the effectiveness of our algorithms on CCGs and ICGs of various sizes. The past decade has seen a surge in the degree of hardware-heterogeneous device interconnectivity, Additionally, there has been a proliferation of resource-constrained wireless sensor networks. Thus, the problems of finding valid paths in CCGs and ICGs are of interest. Nodes in wireless sensor networks can be faulty, vulnerable, energy-constrained, or untrusted. These nodes may also have differing degrees of performance or power efficiency. This can pose a significant challenge in routing the flow of information between nodes. The heterogeneity of nodes in such networks is captured through the use of CCGs and ICGs.

A Reinforcement Learning-Based Dynamic Clustering Algorithm for Compressive Data Gathering in Wireless Sensor Networks

Compressive data gathering (CDG) is an effective technique to handle large amounts of data transmissions in resource-constrained wireless sensor networks (WSNs). However, CDG with static clustering cannot adapt to time-varying environments in WSNs. In this paper, a reinforcement learning-based dynamic clustering algorithm (RLDCA) for CDG in WSNs is proposed. It is a dynamic and adaptive clustering method aiming to further reduce data transmissions and energy consumption in WSNs. Sensor nodes act as reinforcement learning (RL) agents which can observe the environment and dynamically select a cluster to join in. These RL agents are instructed by a well-designed reward scheme to join a cluster with strong data correlation and proper distance. It is also a distributed and lightweight learning method. All agents are independent and operate in parallel. Additional overheads introduced by RL are lightweight. Computations of a linear reward function and a few comparison operations are needed. It is implementable in WSNs. Simulations performed in MATLAB validate the effectiveness of the proposed method and simulation results show that the proposed algorithm achieves the desired effect as well as fine convergence. It decreases data transmissions by 16.6% and 54.4% and energy consumption by 6% and 29%, respectively, compared to the two contrastive schemes.

A novel slot scheduling technique for duty-cycle based data transmission for wireless sensor network

The duty cycling process involves turning a radio into an active and dormant state for conserving energy. It is a promising approach for designing routing protocols for a resource-constrained Wireless Sensor Networks (WSNs). In the duty cycle-based WSNs, the network lifetime is improved and the network transmission is increased as compared to conventional routing protocols. In this study, the active period of the duty cycle is divided into slots that can minimize the idle listening problem. The slot scheduling technique helps determine the most efficient node that uses the active period. The proposed routing protocol uses the opportunistic concept to minimize the sender waiting problem. Therefore, the forwarder set will be selected according to the node's residual active time and energy. Further, the optimum routing path is selected to achieve the minimum forwarding delay from the source to the destination. Simulation analysis reveals that the proposed routing scheme outperforms existing schemes in terms of the average transmission delay, energy consumption, and network throughput.

Energy-efficient time synchronisation based on odd-even periodic data transmission and acknowledgement in wireless sensor networks

In the working process of wireless sensor networks (WSNs), it is challenging to realise precise and energy-efficient time synchronisation on resource-constrained WSNs nodes. In this paper, a fusion method of time synchronisation with periodical data transmission and acknowledgement is proposed for WSNs. Moreover, different response intervals are generated according to the parity characteristic of the measurement data's serial number. The maximum likelihood estimators (MLE) of clock skew and fixed delay, and the simplified MLE of clock skew under the Gaussian random delay are derived. Meanwhile, the corresponding Cramer-Rao Lower Bound is calculated. The two proposed MLEs of clock skew show that both of them can work independently of the fixed delay. Simulation results verify the efficiency of these two clock skew estimators and fixed delay estimator, especially the performance and effectiveness of the second simple and practical MLE.

IPSadas: Identity‐privacy‐aware secure and anonymous data aggregation scheme

Intelligent systems are technologically advanced machines that can sense and respond to the surrounding environment. They have been widely used in medicine, military, transportation, automation, and other fields. However, when these systems deal with their environments, problems such as leakage of identities may occur. The adversary can damage the system communication and attack important nodes. To handle resource-constrained wireless sensor network environments, we propose a secure and anonymous data aggregation scheme. First, based on the bilinear mapping operation and onion routing concepts, we propose a key negotiation and secure information transmission scheme, which conducts confidential transmission and anonymous forwarding of messages in data aggregation. Second, an aggregation routing scheme based on link direction and residual energy is proposed to pledge messages that can arrive the base station without passing through many nodes, which saves network resources to a certain extent. Third, on the basis of the first two contributions, we propose an identity-privacy-aware secure and anonymous data aggregation scheme that protects the identity's privacy. This scheme can conceal the real identity of important nodes and protect the anonymity of messages and link relationships. In addition, an anonymous identity update and synchronization scheme is also proposed to ensure the reliability and security of communication. Meanwhile, our performance evaluations and simulations show that the proposed framework is more effective than several standard schemes with respect to the ability against various attacks, security, and overhead.

A Pairing-Free Identity-Based Identification Scheme with Tight Security Using Modified-Schnorr Signatures

The security of cryptographic schemes is proven secure by reducing an attacker which breaks the scheme to an algorithm that could be used to solve the underlying hard assumption (e.g., Discrete Logarithm, Decisional Diffie–Hellman). The reduction is considered tight if it results in approximately similar probability bounds to that of solving the underlying hard assumption. Tight security is desirable as it improves security guarantees and allows the use of shorter parameters without the risk of compromising security. In this work, we propose an identity-based identification (IBI) scheme with tight security based on a variant of the Schnorr signature scheme known as TNC signatures. The proposed IBI scheme enjoys shorter parameters and key sizes as compared to existing IBI schemes without increasing the number of operations required for its identification protocol. Our scheme is suitable to be used for lightweight authentication in resource-constrained Wireless Sensor Networks (WSNs) as it utilizes the lowest amount of bandwidth when compared to other state-of-the-art symmetric key lightweight authentication schemes. Although it is costlier than its symmetric key counterparts in terms of operational costs due to its asymmetric key nature, it enjoys other benefits such as decentralized authentication and scalable key management. As a proof of concept to substantiate our claims, we perform an implementation of our scheme to demonstrate its speed and memory usage when it runs on both high and low-end devices.

Automatic recognition system of pointer meters based on lightweight CNN and WSNs with on-sensor image processing

The pointer meter is widely used in the modern industrial process. This paper proposes a novel pointer meter recognition method based on wireless sensor networks (WSNs) and a lightweight convolutional neural network (CNN), which completes image preprocessing, CNN classification, and reading calculation on the WSN end node, and then only transmits the recognized result in the WSN to reduce its payload transmission data. Meanwhile, a lightweight CNN classifier model with a simple structure and small size is designed for embedding in the resource-constrained WSN node. A set of experiments have been carried out on the fabricated prototype to verify the feasibility and adaptability of the proposed method. Experimental results have shown that the maximum error of the recognized results for real-world applications is around 0.27%, while the payload transmission data of the WSN decrease from 112.5 kB to 5 bytes.