Resource-limited Wireless Sensor Networks Research Articles

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.

Modified elliptic curve cryptography for efficient data protection in wireless sensor network

Wireless Sensor Networks (WSN) have a crucial function in gathering and transmitting data in different applications, making data protection a top priority. Elliptic Curve Cryptography (ECC) is known for its strong security features, but it requires substantial computational resources in resource-limited WSN environments. To tackle this challenge, this study presents a new alteration to ECC by employing a Point Compression Algorithm, specifically utilizing the Modified Jacobian coordinates. This study assesses the effectiveness of the proposed Modified ECC (MECC) by analyzing key performance metrics such as efficiency, scalability, and communication overhead. These metrics are crucial for ensuring the successful implementation of data protection in WSN. The analysis provides a thorough examination of performance, specifically highlighting encryption time, decryption time, key generation time, and key size. It offers a comprehensive comparison with the conventional ECC approach. The results demonstrate that the MECC, incorporating the Point Compression Algorithm in Modified Jacobian coordinates, presents a favorable approach for improving data security in WSN. The enhanced efficacy, diminished communication burden, and expandability of the suggested methodology are apparent, showcasing its capability to safeguard data transmission while conserving valuable network resources. This research aims to enhance the security of WSN making them more robust and flexible in meeting the changing demands of modern data protection needs.

Efficient key revocation in WSN with lattice-based cryptography

Strong security is crucial as Wireless Sensor Network (WSN) become more common. Dynamic Lattice-Based Key Revocation (DLBKR) is a novel method for improving key revocation in WSN. Lattice-based cryptography helps DLBKR overcome compromised keys and evolving security threats in dynamic sensor networks. Key revocation mechanisms are examined in detail, with a focus on their limitations in WSN. Due to these networks limited resources and ever-changing structure, we propose DLBKR as a novel way to minimize the security risks of compromised keys. DLBKR is featured as a flexible and reactive solution due to its ability to adapt to threat levels. Simulated network sizes from 250 to 2000 nodes were used to evaluate DLBKR. Traditional methods like CRL and OCSP were compared. Latency, revocation efficiency, computational overhead, communication overhead, and key replacement delay were assessed. DLBKR outperforms CRL and OCSP in all parameters, proving its efficiency in dynamic and resource-limited WSN key revocation. This method performs well and offers a flexible framework that incorporates with sensor networks. The DLBKR method help WSN to manage key complexity.

Anonymity preserving lightweight authentication protocol for resource-limited wireless sensor networks

Wireless sensor networks have been deployed in areas such as healthcare, military, transportation and home automation to collect data and forward it to remote users for further processing. Since open wireless communication channels are utilized for data transmissions, the exchanged messages are vulnerable to various threats such as eavesdropping and message falsifications. Therefore, many security solutions have been introduced to address these challenges. However, the resource-constrained nature of the sensor nodes makes it inefficient to deploy the conventional security schemes which require long keys for improved security. Therefore, lightweight authentication protocols have been presented. Unfortunately, majority of these schemes are still insecure while others incur relatively higher energy, computation, communication and storage complexities. In this paper, a protocol that deploys only lightweight one-way hashing and exclusive OR operations is presented. Its formal security analysis using Real-or Random (ROR) model demonstrates its capability to uphold the security of the derived session keys. In addition, its semantic security evaluation shows that it offers user privacy, anonymity, untraceability, authentication, session key agreement and key secrecy. Moreover, it is shown to resist attacks such as side-channeling, physical capture, eavesdropping, offline guessing, spoofing, password loss, session key disclosure, forgery and impersonations. In terms of performance, it has relatively lower communication overheads and improves the computation costs and supported security characteristics by 31.56% and 33.33% respectively.

Protecting Location Privacy in IoT Wireless Sensor Networks through Addresses Anonymity

Location privacy is very important for event-triggered type of Wireless Sensor Networks (WSNs) applications such as tracking and monitoring of wild animals. Most of the security schemes for WSNs are designed to provide protection for content privacy. Contextual privacy such as node identity anonymity has received much less attention. The adversary can fully explore such contextual information to disclose the location of critical components such as source nodes or base station. Most existing schemes provide location privacy at network layer. As no measures are taken to provide node identity anonymity at data link layer, the adversary can launch traffic analysis attacks to jeopardize location privacy. In this paper, a scheme named HASHA is proposed to defend against traffic analysis attacks through hashed one-time addresses. Hashed results of payload are used to create dynamic one-time MAC addresses between the communication pairs. Because of inevitable wireless frame errors, it is impossible for adversaries to track dynamic addresses. Therefore, HASHA can provide strong node identity anonymity, which makes traffic analysis attacks much more difficult and provides better location privacy. Simulations and analysis results show that HASHA can provide better location privacy with limited communication overheads, which is particularly suitable for resource-limited WSNs.

A Survey of Compressive Data Gathering in WSNs for IoTs

Internet of Things (IoTs) are increasingly widespread in the field of health care, smart city and smart home application, industrial and agricultural monitoring, automation, etc. With its growing scale of networks, there are a large amount of data in IoTs needing to be sensed, transmitted, and processed. Resource-limited Wireless Sensor Networks (WSNs) as a perceptual layer of IoTs are hard to handle massive uncompressed sensing data. Compressive data gathering (CDG), which applies compressive sensing theory to data gathering, is a perfectly matching method for data compressing and gathering in WSNs. This promising method has attracted lots of researchers’ attention. In this paper, we attempt to survey substantial references about CDG in WSNs. According to their technology schemes, we classify the published references into three categories, i.e., routing protocol of CDG, clustering scheme of CDG, and CDG combined with other technologies. The merits and defects of each method are highlighted. Our work aims to provide an insight into CDG and promote improvements of this technology.

Renewable Energy Sources Efficient Detection in Triangulation for Wireless Sensor Networks

It is important for wireless sensor networks (WSNs) to determine whether hubs can be restricted, called localizability recognition. This progression is important for confining hubs, achieving minimal organizations of effort and distinguishing critical in field-based applications. Because of their high calculation and communication costs, centralized graph algorithms are meaningless to an asset-restricted WSN, whereas distributed methodologies can skip a large number of hypothetically locatable hubs in a resource-limited WSN. In this document we are proposing a efficient and successful distributed methodology to solve that address of issue. In addition, we demonstrate our calculation accuracy and analyze the reasons why our calculation will discover increasingly localizable hubs while requiring less established area hubs. The paper introduces a new RSSI-based localization approach triangle as well as centroid placement, utilizing triangle as well as centroid approach to minimize RSSI measurement error. Modelling simulations show that this algorithm can significantly increase the precision of the location compared to the trilateration.

A Cluster-Head Rotating Election Routing Protocol for Energy Consumption Optimization in Wireless Sensor Networks

Balancing energy consumption using the clustering routing algorithms is one of the most practical solutions for prolonging the lifetime of resource-limited wireless sensor networks (WSNs). However, existing protocols cannot adequately minimize and balance the total network energy dissipation due to the additional tasks of data acquisition and transmission of cluster heads. In this paper, a cluster-head rotating election routing protocol is proposed to alleviate the problem. We discovered that the regular hierarchical clustering method and the scheme of cluster-head election area division had positive effects on reducing the energy consumption of cluster head election and intracluster communication. The election criterion composed of location and residual energy factor was proved to lower the probability of premature death of cluster heads. The chain multihop path of intercluster communication was performed to save the energy of data aggregation to the base station. The simulation results showed that the network lifetime can be efficiently extended by regulating the adjustment parameters of the protocol. Compared with LEACH, I-LEACH, EEUC, and DDEEC, the algorithm demonstrated significant performance advantages by using the number of active nodes and residual energy of nodes as the evaluation indicators. On the basis of these results, the proposed routing protocols can be utilized to increase the capability of WSNs against energy constraints.

A Novel Association Rule-Based Data Mining Approach for Internet of Things Based Wireless Sensor Networks

Wireless Sensor Network (WSN) is one of the fundamental technologies used in the Internet of Things (IoT) which is deployed for diverse applications to carry out precise real-time observations. The limited resources of WSN with massive volume of fast-flowing IoT data make the aggregation and analytics of data more challenging. Recently, data mining-based solutions have been proposed to effectively handle the data being generated by the sensors and to analyze the data patterns for deducing the required information from it. The increasing need of these techniques motivated us to propose a distributed and efficient data mining technique that not only handles the massive and rapidly generated data by the nodes, but also increases the life span of the network. In this paper, we propose a novel scheme for the IoT based WSN that mines the sensor data using association rule without moving it to any Cluster Head (CH) or Base Station (BS). The new proposed scheme enables sensors to perform computations locally and only the minimum higher-level statistical summaries of the data at Cluster Members (CMs) are exchanged with their CH. This considerably reduces the communication overhead which ultimately prolongs the network lifetime. The proposed scheme is evaluated via extensive simulations and the results obtained demonstrate that the integration of the proposed scheme in the existing protocols significantly reduces the communication overhead which ultimately prolongs the network lifetime and stability.

Accurate Analysis of Target Data and Transmission of Measuring Information Security by Airborne Photoelectric Platform

Unmanned reconnaissance aircraft can acquire data by remote sensing. The photoelectric imaging platform of unmanned aerial vehicle (UAV) is mounted on the UAV, which uses photogrammetry, image processing and other technologies to accurately analyze the target of interest. With the continuous development of technology, people's requirement for target positioning accuracy is increasing. At the same time, the airborne optoelectronic platform meets the characteristics of wireless sensor networks and needs to deploy key management scheme to ensure the security of data transmission. In this study, coordinate transformation method is used to generate a photoelectric imaging target positioning scheme, based on which the factors affecting the accuracy of target data are analyzed. Using the idea of Monte Carlo algorithm and MatLab simulation, the attitude angle of aircraft and the angle error of gyroscope affect the accuracy of target data. In order to improve the security of data transmission, the identity authentication scheme of Elliptic Curve (ECC) is taken as the research object of key management scheme in this study. Considering the limited resources of wireless sensor networks, a combined public key + identity encryption scheme is proposed. In the simulation process, the scheme shows good performance in encryption/decryption overhead, and the length of ciphertext/private key is controllable. This research is helpful for the airborne photoelectric platform to measure the target data better and ensure the security of data transmission.

An efficient load balancing and performance optimization scheme for constraint oriented networks

A constraint oriented wireless sensor network (WSN) faces a challenging task of handling different issues i.e., routing mechanism, coverage of the underlined sensing fields for as long as possible, energy efficiency, reliable transmission of packets, congestion controls etc. Load balancing or routing protocols, which are specifically designed for the resource limited sensor networks, resolve some of these issues. However, most of these mechanisms were either applications specific or overlay complex. Moreover, these techniques underestimated an important aspect of WSNs i.e., sensor nodes criticality or vulnerability factor that is defined as the importance of a node from the network connectivity perspective. In this paper, an efficient load balancing and performance optimization scheme (LBS) is presented to address these issues such as throughput, long term connectivity, end to end delay and energy efficiency in resource limited WSNs. The proposed LBS technique conflates a sensor node criticality factor, residual energy and hop-count to form an optimized load balancing strategy. Initially, the proposed LBS routes most of the load or traffic on the shortest paths by assigning a highest weight-age factor, 80%, to the hop count metric and lower weight-age to both criticality and residual energy metrics i.e., 20%. Load balancing strategy is revised if one or more nodes deplete 50% of their on-board batteries. Simulation results verify that the proposed LBS scheme outperforms the existing field proven approaches i.e., shortest path, vulnerability aware routing, energy based spreading and multiple path based load balancing schemes.

Cost-Aware Stochastic Compressive Data Gathering for Wireless Sensor Networks

Data gathering is a crucial function of wireless sensor networks (WSNs). In resource-limited WSNs, it is critical to improve cost-efficiency and prolong network lifetime. Sensor networks utilizing deterministic routing paths are particularly vulnerable to attacks. Additionally, repeated use of the same path will introduce load unbalance. In this paper, we propose a cost-aware stochastic compressive data gathering for WSNs. In contrast to traditional compressive sensing (CS) based algorithms that implicitly assume uniform transmission cost, our proposed scheme will consider the cost diversity into the CS data gathering framework. The Markov chain-based model will be used to characterize the stochastic data gathering process. An optimization problem is formulated to minimize the total expected cost subjected to the constraints on global degree of randomness and the recovery error. Our proposed algorithm requires less total expected cost to achieve certain level of recovery accuracy. It also prolongs network lifetime due to its load balancing features. Extensive simulations on both synthetic and real data show that the proposed algorithm significantly outperforms benchmark algorithms.

Regulating the Packet Transmission Cost of Source Location Privacy Routing Schemes in Event Monitoring Wireless Networks

Sensor nodes in wireless sensor networks (WSNs) are usually battery-operated and resource-constrained. The sensor nodes are often deployed in remote areas where the batteries cannot easily be recharged or replaced. Consequently, power becomes a limited resource in WSNs. Thus, energy consumption of the sensor nodes is among parameters of paramount importance. Subsequently, source location privacy (SLP) routing schemes must be energy-efficient and overall cost-effective. Angle-based routing schemes can cost-effectively protect the SLP. The goal of this study is to propose a new path node offset angle routing algorithm to improve the packet transmission cost of two existing SLP routing schemes. The proposed algorithm considers path node offset angles, arbitrary factors, and contrived regions to compute relatively short but greatly randomized routing paths. The routes offer a reduced number of packet forwarding events in the near-sink region and eventually diminish the packet transmission cost. Performance analysis results verify that the proposed path node offset angle routing algorithm effectively improves the packet transmission cost of the schemes and guarantees strong SLP protection throughout the WSN domain. Furthermore, the routing algorithm is capable of alleviating the energy-hole problem.

DV-Hop localization algorithm based on bacterial foraging optimization for wireless multimedia sensor networks

DV-Hop localization algorithm is a classic range free localization algorithm in wireless sensor networks (WSNs). Although easy to be employed in low cost and resource limited WSNs, DV-Hop localization algorithm suffers from low localization accuracy as the other range free localization approaches. To improve the localization accuracy, in this paper we introduce Bacterial Foraging Optimization (BFO), an efficient optimization method that has been widely applied in a variety of scientific and engineering applications. We conduct extensive simulations under different network setting, the simulation results demonstrate that the proposed algorithm achieves significantly higher positioning accuracy than the basic DV-Hop algorithm.

Triangle Extension: Efficient Localizability Detection in Wireless Sensor Networks

Determining whether nodes can be localized, called localizability detection, is essential for wireless sensor networks (WSNs). This step is required for localizing nodes, achieving low-cost deployments, and identifying prerequisites in location-based applications. Centralized graph algorithms are inapplicable to a resource-limited WSN because of their high computation and communication costs, whereas distributed approaches may miss a large number of theoretically localizable nodes in a resource-limited WSN. In this paper, we propose an efficient and effective distributed approach in order to address this problem. Furthermore, we prove the correctness of our algorithm and analyze the reasons our algorithm can find more localizable nodes while requiring fewer known location nodes than existing algorithms, under the same network configurations. The time complexity of our algorithm is linear with respect to the number of nodes in a network. We conduct both simulations and real-world WSN experiments to evaluate our algorithm under various network settings. The results show that our algorithm significantly outperforms the existing algorithms in terms of both the latency and the accuracy of localizability detection.

Improve security of wireless sensor networks through reluctant checksum

The open wireless channel makes the data prone to being eavesdropped. Current wireless security schemes are designed to protect data through cryptography. But an adversary can still recover the secrets by eavesdropping the frames and performing off-line brute-force attacks. Capturing valid frames is fundamental for such attacks. It is generally accepted that each frame at data link layer is designed to include cyclic redundancy check (CRC) checksum sequence for integrity check. However, frame checksum sequence also helps adversary to capture correct frames and drop corrupted ones, which are fundamental to future off-line attacks. In this article, we argue that from the perspective of wireless security, it is unreasonable to include checksum sequence in data link layer frame without any protection. A reluctant checksum scheme named R-CS is proposed. Based on the inherent characteristic of wireless networks that frame error is inevitable, checksum of frame is protected by accumulated checksum algorithm in R-CS. The checksum of the frame cannot be decoded by any nodes except the receiver. Without checksum, adversaries cannot distinguish error frames from correct ones. R-CS requires little computation and communication resources, which is particularly suitable for resource-limited wireless sensor network. Our experimental results clearly demonstrate that R-CS is feasible for wireless sensor network.

Cognitive Radio Sensor Network With Green Power Beacon

In this paper, we propose a cognitive radio sensor network with green power beacon (PB) to solve spectrum and energy scarcity problems in the resource-limited wireless sensor networks. In the proposed model, a PB node harvests energy from ambient sources and transmits the harvested energy to cognitive sensor node (CSN) through in-band energy transmission. The CSN uses the received energy to transfer its data to the sink. Resource-constrained condition of sensor network motivates us to deploy random spectrum access scheme to control the interference between primary and secondary networks, and also self-interference inside the secondary network (i.e., between the CSN and the PB). This spectrum access scheme benefits from low hardware complexity and energy consumption. Using tools from queueing theory, we analyze the service rate of the CSN and primary network for both limited-battery as well as infinite-battery CSN. Then, by optimizing the spectrum access parameters of the PB and the CSN, service rate of the CSN is maximized, while the quality of service (QoS) constraint of primary network is satisfied. For each energy harvesting rate and QoS constraint, it is determined whether energy or spectrum or both are limiting the performance of the CSN. This result can be used to adopt proper solutions for improving the performance of the network.

Joint Design of Energy-Efficient Clustering and Data Recovery for Wireless Sensor Networks

The two-layer network structure has been widely adopted in wireless sensor networks (WSNs) for managing sensor nodes. In such a structure, the low layer nodes communicate with their cluster head, followed by the cluster-head nodes communicating with the base station operating in either a one-hop or a multi-hop manner. The main focus of node-clustering algorithms is minimizing energy consumption due to strictly limited resources in WSNs. Also, WSNs are data intensive networks with the capability of providing users with accurate data. Unfortunately, data missing is common in WSNs. In this paper, we propose a novel joint design of sensor nodes clustering and data recovery, where the WSNs is organized in a two-layer manner with our developed clustering algorithm, and then, the missing data are recovered based on this two-layer structure. Furthermore, in the proposed clustering algorithm, we take both the energy-efficiency and data forecasting accuracy into consideration and investigate the tradeoff between them. This is based on the key observation that the high energy-efficiency of the network can be achieved by reducing the distances among the nodes in a cluster, while the accuracy of the forecasting results can be improved by increasing the correlation of the data stream among the nodes in a cluster. Simulation results demonstrate that our joint design outperforms the existing algorithms in terms of energy consumption and forecasting accuracy.

A Survey on Cross-Layer Quality-of-Service Approaches in WSNs for Delay and Reliability-Aware Applications

Using wireless sensor networks (WSNs) in delay and reliability critical environments is highly desired due to their unique advantages such as low cost, ease of deployment, and redundancy. However, successful deployment of resource-limited WSNs in such applications requires strict quality-of-service (QoS) provisioning techniques to meet the desired latency and reliability targets of those applications. Implementation of QoS techniques in WSNs is significantly challenging since WSNs have been initially devised for low-data-rate non-real-time applications. Therefore, WSN designers and developers resort to different cross-layer interaction and optimization techniques to provision QoS in WSNs. In this paper, we present a survey on the state of the art of cross-layer QoS approaches in wireless terrestrial sensor networks to achieve delay and reliability bounds in critical applications. Our paper provides a unique classification of cross-layer QoS approaches in WSNs that allows surveying a large amount of studies with utmost clarity. Furthermore, we highlight the main challenges of implementing QoS protocols in WSNs and present an overview of QoS-aware WSN applications.

Energy-Efficient Node Scheduling Method for Cooperative Target Tracking in Wireless Sensor Networks

Using the sensor nodes to achieve target tracking is a challenging problem in resource-limited wireless sensor networks. The tracking nodes are usually required to consume much energy to improve the tracking performance. In this paper, an energy-efficient node scheduling method is proposed to minimize energy consumption while ensuring the tracking accuracy. Firstly, the Kalman-consensus filter is constructed to improve the tracking accuracy and predict the target position. Based on the predicted position, an adaptive node scheduling mechanism is utilized to adjust the sample interval and the number of active nodes dynamically. Rather than using traditional search algorithm, the scheduling problem is decomposed to decouple the sample interval and number of nodes. And the node index is mapped into real domain to get closed-form solution to decide the active nodes. Thus, the NP-complete nature is avoided in the proposed method. The proposed scheduling method can keep the tracking accuracy while minimizing energy consumption. Simulation results validate its effective performance for target tracking in wireless sensor networks.