Resource-constrained Sensor Nodes Research Articles

Online Adaptive Kalman Filtering for Real-Time Anomaly Detection in Wireless Sensor Networks.

Wireless sensor networks (WSNs) are essential for a wide range of applications, including environmental monitoring and smart city developments, thanks to their ability to collect and transmit diverse physical and environmental data. The nature of WSNs, coupled with the variability and noise sensitivity of cost-effective sensors, presents significant challenges in achieving accurate data analysis and anomaly detection. To address these issues, this paper presents a new framework, called Online Adaptive Kalman Filtering (OAKF), specifically designed for real-time anomaly detection within WSNs. This framework stands out by dynamically adjusting the filtering parameters and anomaly detection threshold in response to live data, ensuring accurate and reliable anomaly identification amidst sensor noise and environmental changes. By highlighting computational efficiency and scalability, the OAKF framework is optimized for use in resource-constrained sensor nodes. Validation on different WSN dataset sizes confirmed its effectiveness, showing 95.4% accuracy in reducing false positives and negatives as well as achieving a processing time of 0.008 s per sample.

A Novel Method for Energy Efficient Clustering in Wireless Sensor Networks

Wireless Sensor Networks (WSNs) play a crucial role in various applications, including environmental monitoring, industrial automation, and healthcare. However, optimizing WSNs for efficient resource utilization, energy conservation, and reliable data transmission remains a challenging task due to the dynamic nature of the network environment and resource-constrained sensor nodes. In this study, we propose a Hybrid Firefly Genetic Algorithm (HFGA) for optimizing WSN performance. The HFGA combines the strengths of the firefly algorithm's global search capabilities and the genetic algorithm's local search and optimization efficiency. By integrating these two evolutionary algorithms, the HFGA aims to achieve superior performance in terms of energy efficiency, network coverage, and convergence speed. We evaluate the effectiveness of the proposed HFGA through extensive simulation experiments in various WSN scenarios. The results demonstrate that the HFGA outperforms traditional optimization approaches and baseline algorithms in optimizing WSN performance metrics. Furthermore, we discuss the practical implications and future research directions for deploying the HFGA in real-world WSN applications. Overall, this study contributes to advancing WSN optimization techniques and enhancing the reliability and efficiency of WSN deployments. Keywords: Clustering, Genetic algorithm, Firefly algorithm, FAG algorithm.

Latency and Residual Energy Analysis of MIMO Heterogeneous Wireless Sensor Networks

Energy-constrained Wireless Sensor Networks (WSNs) have garnered significant research interest in recent years. Multiple-Input Multiple-Output (MIMO), or Cooperative MIMO, represents a specialized application of MIMO technology within WSNs. This approach operates effectively, especially in challenging and resource-constrained environments. By facilitating collaboration among sensor nodes, Cooperative MIMO enhances reliability, coverage, and energy efficiency in WSN deployments. Consequently, MIMO finds application in diverse WSN scenarios, spanning environmental monitoring, industrial automation, and healthcare applications. This research paper presents a comparative performance analysis of MIMO wireless sensor networks and traditional wireless sensor networks without MIMO using Network Simulator NS2.35 for analysis of End to End Delay for packet transmission and Residual energy of nodes. The research work shows application of MIMO in Wireless Sensor Networks with considerable improvements in Quality of Service parameters which is achieved through Spatial Multiplexing and Diversity Gain. MIMO enables multiple spatial streams, allowing several data streams to be transmitted simultaneously on the same channel. This increases the overall throughput as multiple sensors can transmit their data concurrently without interference. MIMO systems also provide diversity gain by transmitting multiple copies of the same data over different antennas which helps in mitigating the effects of fading and interference, resulting in a more reliable and higher-throughput communication link as compared to a SISO channel. Another advantage of employing MIMO in WSN is reduction in End-to-End delays in data transmission. Last but not the least, MIMO can be configured to optimize the power consumption of individual sensors by adjusting the number of antennas used and transmission power levels based on channel conditions. Hence, MIMO can help to extend the network's lifetime by conserving energy in resource-constrained sensor nodes by preservation of Residual Energy.

A Comparative Study on Software-Defined Network with Traditional Networks

The emergence of a technological age, in which nearly everything is linked and accessible from anywhere, is largely due to the Internet. Hence, traditional Networks continue to be difficult and complex to operate despite their widespread deployment. As a consequence, it is challenging to set up the networks in accordance with the established protocols and respond to variations in load and defects by reconfiguring the structure. The data and control layers are packaged together in the contemporary systems, further complicating problems by vertically integrating them. Because of their limited resources and unsecured transmission channel, wireless sensor networks (WSNs) are particularly vulnerable to persistent security attacks. Many hundreds of self-organized and resource-constrained sensor nodes make up the WSN. It is possible to build an ad-hoc network of sensors without a specified architecture or centralised control because these sensor nodes are often organised in a scattered form. The underlying problem is to strengthen the privacy enforcement in these systems because WSNs will have power over real-time applications, where unauthorized conduct might lead to significant harm. In order to address this issue, the software-defined network (SDN) technology was developed. With the creation of SD, network operators now have more legitimacy and influence over the networks. Based on a global perspective and centralised management of the network topology, SDN has increased the rigour of its enhancing security. This study describes the software-defined network, outlining its fundamental ideas, how it differs from conventional networking, and its architectural tenets. Also, it highlighted the key benefits of SDN while emphasizing availability, maliciousness, packet delivery and duplication ratio, and efficiency.

Unified ensemble federated learning with cloud computing for online anomaly detection in energy-efficient wireless sensor networks

Anomaly detection in Wireless Sensor Networks (WSNs) is critical for their reliable and secure operation. Optimizing resource efficiency is crucial for reducing energy consumption. Two new algorithms developed for anomaly detection in WSNs—Ensemble Federated Learning (EFL) with Cloud Integration and Online Anomaly Detection with Energy-Efficient Techniques (OAD-EE) with Cloud-based Model Aggregation. EFL with Cloud Integration uses ensemble methods and federated learning to enhance detection accuracy and data privacy. OAD-EE with Cloud-based Model Aggregation uses online learning and energy-efficient techniques to conserve energy on resource-constrained sensor nodes. By combining EFL and OAD-EE, a comprehensive and efficient framework for anomaly detection in WSNs can be created. Experimental results show that EFL with Cloud Integration achieves the highest detection accuracy, while OAD-EE with Cloud-based Model Aggregation has the lowest energy consumption and fastest detection time among all algorithms, making it suitable for real-time applications. The unified algorithm contributes to the system's overall efficiency, scalability, and real-time response. By integrating cloud computing, this algorithm opens new avenues for advanced WSN applications. These promising approaches for anomaly detection in resource constrained and large-scale WSNs are beneficial for industrial applications.

Performance Analysis of MIMO Heterogeneous Wireless Sensor Networks

Wireless Sensor Networks (WSN) are widely used in remote applications related to defence and healthcare. A network with nodes having different capabilities like sensing, various computational capabilities, power-efficient communication, and a varied sensing range is called a heterogeneous wireless sensor network. Heterogeneous wireless sensor networks using MIMO wireless channels are more useful for energy-efficient multi-channel communication. MIMO applications in wireless sensor networks have the potential to enhance throughput, reduce End-to-End Delay, improve packet delivery ratios, and conserve energy in wireless sensor networks. Its implementation needs to be carefully considered in light of the specific deployment conditions and resource constraints of the network, considering proper antenna design, synchronisation mechanisms, and energy-efficient algorithms. This paper presents a comparative performance analysis of MIMO wireless sensor networks and traditional wireless sensor networks without MIMO for various Quality of Service parameters like Packet Delivery Ratio, End to End Delay, Throughput and Residual energy. The research work shows that the application of MIMO in Wireless Sensor Networks enables sensor nodes to collaborate effectively, leading to improved reliability and coverage, and also increases the network's lifetime by conserving energy in resource-constrained sensor nodes through the preservation of Residual Energy.

AccFlow: Defending against the Low-Rate TCP DoS Attack in Drones

As drones are widely employed in various industries and daily life, concerns regarding their safety have been gradually emerging. Denial of service (DoS) attacks have become one of the most significant threats to the stability of resource-constrained sensor nodes. Traditional brute-force and high-rate distributed denial of service (DDoS) attacks are easily detectable and mitigated. However, low-rate TCP DoS attacks can considerably impair TCP throughput and evade DoS prevention systems by inconspicuously consuming a small portion of network capacity, and whereas the literature offers effective defense mechanisms against DDoS attacks, there is a gap in defending against Low-Rate TCP DoS attacks. In this paper, we introduce AccFlow, an incrementally deployable Software-Defined Networking (SDN)-based protocol designed to counter low-rate TCP DoS attacks. The main idea of AccFlow is to make the attacking flows accountable for the congestion by dropping their packets according to their loss rates. AccFlow drops their packets more aggressively as the loss rates increase. Through extensive simulations, we illustrate that AccFlow can effectively safeguard against low-rate TCP DoS attacks, even when attackers employ varying strategies involving different scales and data rates. Furthermore, whereas AccFlow primarily addresses low-rate TCP DoS attacks, our research reveals its effectiveness in defending against general DoS attacks. These general attacks do not rely on the TCP retransmission timeout mechanism but rather deplete network resources, ultimately resulting in a denial of service for legitimate users. Additionally, we delve into the scalability of AccFlow and its viability for practical deployment in real-world networks. Finally, we demonstrate the effectiveness of AccFlow in safeguarding network resources.

Truthful and Optimal Data Preservation in Base Station-less Sensor Networks: An Integrated Game Theory and Network Flow Approach

We aim to preserve a large amount of data generated inside base station-less sensor networks (BSNs) while considering that sensor nodes are selfish. BSNs refer to emerging sensing applications deployed in challenging and inhospitable environments (e.g., underwater exploration); as such, there do not exist data-collecting base stations in the BSN to collect the data. Consequently, the generated data has to be stored inside the BSN before uploading opportunities become available. Our goal is to preserve the data inside the BSN with minimum energy cost by incentivizing the storage- and energy-constrained sensor nodes to participate in the data preservation process. We refer to the problem as DPP: d ata p reservation p roblem in the BSN. Previous research assumes that all the sensor nodes are cooperative and that sensors have infinite battery power and design a minimum-cost flow-based data preservation solution. However, in a distributed setting and under different control, the resource-constrained sensor nodes could behave selfishly only to conserve their resources and maximize their benefit. In this article, we first solve DPP by designing an integer linear programming (ILP)-based optimal solution without considering selfishness. We then establish a game-theoretical framework that achieves provably truthful and optimal data preservation in BSNs. For a special case of DPP wherein nodes are not energy-constrained, referred to as DPP-W, we design a data preservation game DPG-1 that integrates algorithmic mechanism design (AMD) and a more efficient minimum cost flow-based data preservation solution. We show that DPG-1 yields dominant strategies for sensor nodes and delivers truthful and optimal data preservation. For the general case of DPP (wherein nodes are energy-constrained), however, DPG-1 fails to achieve truthful and optimal data preservation. Utilizing packet-level flow observation of sensor node behaviors computed by minimum cost flow and ILP, we uncover the cause of the failure of the DPG-1. It is due to the packet dropping by the selfish nodes that manipulate the AMD technique. We then design a data preservation game DPG-2 for DPP that traces and punishes manipulative nodes in the BSN. We show that DPG-2 delivers dominant strategies for truth-telling nodes and achieves provably optimal data preservation with cheat-proof guarantees. Via extensive simulations under different network parameters and dynamics, we show that our games achieve system-wide data preservation solutions with optimal energy cost while enforcing truth-telling of sensor nodes about their private cost types. One salient feature of our work is its integrated game theory and network flows approach. With the observation of flow level sensor node behaviors provided by the network flows, our proposed games can synthesize “microscopic” (i.e., selfish and local) behaviors of sensor nodes and yield targeted “macroscopic” (i.e., optimal and global) network performance of data preservation in the BSN.

Could Tierless Languages Reduce IoT Development Grief?

Internet of Things (IoT) software is notoriously complex, conventionally comprising multiple tiers. Traditionally an IoT developer must use multiple programming languages and ensure that the components interoperate correctly. A novel alternative is to use a single tierless language with a compiler that generates the code for each component and ensures their correct interoperation. We report a systematic comparative evaluation of two tierless language technologies for IoT stacks: one for resource-rich sensor nodes (Clean with iTask) and one for resource-constrained sensor nodes (Clean with iTask and mTask). The evaluation is based on four implementations of a typical smart campus application: two tierless and two Python-based tiered. (1) We show that tierless languages have the potential to significantly reduce the development effort for IoT systems, requiring 70% less code than the tiered implementations. Careful analysis attributes this code reduction to reduced interoperation (e.g., two embedded domain-specific languages and one paradigm versus seven languages and two paradigms), automatically generated distributed communication, and powerful IoT programming abstractions. (2) We show that tierless languages have the potential to significantly improve the reliability of IoT systems, describing how Clean iTask/mTask maintains type safety, provides higher-order failure management, and simplifies maintainability. (3) We report the first comparison of a tierless IoT codebase for resource-rich sensor nodes with one for resource-constrained sensor nodes. The comparison shows that they have similar code size (within 7%), and functional structure. (4) We present the first comparison of two tierless IoT languages, one for resource-rich sensor nodes and the other for resource-constrained sensor nodes.

Spread Spectrum based QoS aware Energy Efficient Clustering Algorithm for Wireless Sensor Networks

Wireless sensor networks (WSNs) are composed of small, resource-constrained sensor nodes that form self-organizing, infrastructure-less, and ad-hoc networks. Many energy-efficient protocols have been developed in the network layer to extend the lifetime and scalability of these networks, but they often do not consider the Quality of Service (QoS) requirements of the data flow, such as delay, data rate, reliability, and throughput. In clustering, the probabilistic and randomized approach for cluster head selection can lead to varying numbers of cluster heads in different rounds of data gathering. This paper presents a new algorithm called "Spread Spectrum based QoS aware Energy Efficient Clustering for Wireless sensor Networks" that uses spread spectrum to limit the formation of clusters and optimize the number of cluster heads in WSNs, improving energy efficiency and QoS for diverse data flows. Simulation results show that the proposed algorithm outperforms classical algorithms in terms of energy efficiency and QoS.

Trust Evaluation and Decision Based on D-S Evidence Theory: Early Models and Future Perspectives

Due to the technical characteristics and application scenarios of distributed networks, their nodes can easily be invaded and compromised. It will result in information being forged or tampered without difficulty. An effective scheme to guarantee the authenticity and integrity of information is judging how trustworthy nodes are in terms of transmission. In D-S evidence theory (DST), the uncertainty can be expressed to solve the trust fusion issue for multiple nodes. In this paper, for reviewing the DST-based trust evaluation and decision and providing their future research directions systematically. Meanwhile, the DST is briefly reviewed, and two improvements in DST are categorically described. The role and mechanism in DST-based trust models are compared and analyzed. The valuable research directions in the near future are represented. Our contributions could solve the trust problem in resource constrained sensor nodes and improve the decision reliability of network.

A Spherical Band-based DV-Hop localization technique for three-dimensional wireless sensor network

Wireless Sensor Networks have been the focal point of research for many years due to their wide range of application areas. Such networks consist of resource-constrained sensor nodes that are generally not equipped with any positioning component due to cost issues. This requires the adoption of suitable methodologies to infer the location of the deployed sensor nodes. Location information of such sensor nodes can be obtained with the help of some location-aware nodes. Numerous localization algorithms exist in the literature. Amongst them, Distance Vector Hop (DV-Hop) is a computationally less expensive algorithm that uses hop count values between sensor nodes and anchor nodes for location estimation of the deployed sensor nodes. However, the traditional DV-Hop algorithm produces a larger positioning deviation for a higher hop count value. Several existing works attempt to address this issue by either modifying the hop size or optimizing the estimated position resulting in comparatively higher localization errors and computationally expensive. This paper aims to solve the issue by modifying the hop size by dividing it into equal-sized spherical bands (SB). Sensor nodes use this SB value for computing their distances from anchor nodes and non-coplanar anchor nodes for location estimation. The simulation results demonstrate that the mean localization error of the proposed approach has reduced approximately by 75%, 66%, and 47% in comparison to traditional DV-Hop, 3D PSODV-Hop, and 3D GAIDV-Hop respectively.

Energy-efficient data-aggregation for optimizing quality of service using mobile agents in wireless sensor network

<p>Quality of service (QoS) is essential for carrying out data transmission using resource-constrained sensor nodes in wireless sensor network (WSN). The introduction of mobile agent-based data aggregation is reported to offer energy efficiency; however, it has limitations, especially using a single mobile agent, where QoS optimization is not feasible. A review of existing studies showcases some dedicated attempts to use a mobile agent-based approach and address QoS enhancements. However, they were never combined studied. Therefore, this paper introduces a unique concept of retaining maximum QoS performance during data aggregation using a single mobile agent. The model introduces a unique communication framework, transmission provisioning using exceptional routine management, and simplified energy modeling. The proposed model has aimed for a lower delay and faster data aggregation speed with lower consumption of transmittance energy. The implementation and assessment of the model are carried out considering the challenging environment of WSN with multiple scales of data priority. The proposed model also contributes to evolving out with simplified communication vectors in a highly decentralized method. MATLAB's simulation outcome shows that the proposed system offers better delay performance, optimal energy management, and faster response time than existing schemes.</p>

Multinode Data Offloading for Urban Wireless Sensor Networks Based on Fog Computing: A Multiarmed Bandit Approach

Urban wireless sensor networks (UWSNs) are an important application scenario for the Internet of Things (IoT). With the emergence of many computationally intensive applications based on urban environments, sensors in wireless sensor networks are unable to meet demands such as latency due to their limited resources. Fog computing architectures have the potential to liberate data transmission from resource-constrained sensor nodes through data offloading. Therefore, data collection and scalable collaboration based on fog architectures are seen as a challenge. For the multinode data offloading problem, a multinode data offloading strategy based on stable matching and MAB (multi-armed bandit) model is proposed to maximize the offloading success rate while guaranteeing the latency requirements of the source task nodes. Firstly, the multinode data offloading problem is modelled. Secondly, the case of multinode selection conflict is considered, and selection conflict and information exchange are reduced by the MAB model and the fallback timer. Then, an adjustment strategy is proposed based on the idea of delayed reception in the stable matching of game theory. Finally, the multinode data offloading problem is solved by successive iterations. The proposed algorithm not only accomplishes coordinated cooperation between nodes to achieve high-quality data offloading and avoid collisions between nodes but also reduces the amount of information exchanged between nodes. The effectiveness of the algorithm is demonstrated by theoretical analysis and simulation experiments.

Clock Skew Compensation Algorithm Immune to Floating-Point Precision Loss

We propose a novel clock skew compensation algorithm based on Bresenham's line drawing algorithm. The proposed algorithm can avoid the effect of limited floating-point precision (e.g., 32-bit single precision) on clock skew compensation and thereby provide high-precision time synchronization even with resource-constrained sensor nodes in wireless sensor networks.

New Bit Pattern Based IPv6 Address Compression Techniques for 6LoWPAN Header Compression

Things in the world can be connected to the Internet through various technologies such as Wi-Fi, Bluetooth, IEEE 802.15.4 etc. Among all IPv6 over IEEE 802.15.4 looks promising for outdoor environments for connecting a very large number of resource constrained sensor nodes. 6LoWPAN is an adaptation layer to support IPv6 over IEEE 802.15.4 to overcome the challenge of the physical layer with respect to the limitation of 127 bytes of payload. 6LoWPAN supports header compression as one of its functions to reduce the number of bits in header by using compression techniques. Static Context Header Compression (SCHC) provides RuleID based header compression. This paper proposes further compression of address bits based on compressing leading zeros in IPv6 addresses. The proposed work is analysed with respect to Header compression HC1 of 6LoWPAN and SCHC techniques. The simulation results show compression of address bits are 10% to 40% more compared to traditional address compression of the 6LoWPAN address compression when continuous zeroes are present in the address. The compression of address bits provides sufficient space for sending data payload in one frame during communication.

Energy Efficient UAV Flight Path Model for Cluster Head Selection in Next-Generation Wireless Sensor Networks.

Wireless sensor networks (WSNs) are one of the fundamental infrastructures for Internet of Things (IoTs) technology. Efficient energy consumption is one of the greatest challenges in WSNs because of its resource-constrained sensor nodes (SNs). Clustering techniques can significantly help resolve this issue and extend the network’s lifespan. In clustering, WSN is divided into various clusters, and a cluster head (CH) is selected in each cluster. The selection of appropriate CHs highly influences the clustering technique, and poor cluster structures lead toward the early death of WSNs. In this paper, we propose an energy-efficient clustering and cluster head selection technique for next-generation wireless sensor networks (NG-WSNs). The proposed clustering approach is based on the midpoint technique, considering residual energy and distance among nodes. It distributes the sensors uniformly creating balanced clusters, and uses multihop communication for distant CHs to the base station (BS). We consider a four-layer hierarchical network composed of SNs, CHs, unmanned aerial vehicle (UAV), and BS. The UAV brings the advantage of flexibility and mobility; it shortens the communication range of sensors, which leads to an extended lifetime. Finally, a simulated annealing algorithm is applied for the optimal trajectory of the UAV according to the ground sensor network. The experimental results show that the proposed approach outperforms with respect to energy efficiency and network lifetime when compared with state-of-the-art techniques from recent literature.

Secure user authentication mechanism for IoT-enabled Wireless Sensor Networks based on multiple Bloom filters

In this article, we propose an efficient Bloom filter and fuzzy extractor based user authentication technique, which is significant for an Internet of Things (IoT)-enabled multi-hop Wireless Sensor Networks (IoT-WSNs). The novelty of the proposed authentication technique is that it prevents fraudulent querying data delivery at the starting phase (i.e., at the querying sensor node itself) to stop false or counterfeit data flooding (which can exhaust the resources of IoT-WSNs) from the resource-constrained IoT-enabled sensor nodes to the nearby gateway node. The proposed authentication technique has the ability to significantly reduce the Bloom filter based false positive for user authentication in IoT-WSNs, while also preserving the constraint resources of WSNs. To estimate the security robustness of the proposed protocol based on the security goals of IoT-WSNs, we perform the formal security analysis using the random oracle and Real-Or-Random (ROR) models, informal security analysis and also the formal security verification using a widely-recognized automated software validation tool, known as Automated Validation of Internet Security Protocols and Applications (AVISPA). Next, communication and computational overheads analysis reveal that the proposed protocol is appropriate for resource-constrained sensor nodes. Furthermore, a correlative security feature and complexity analysis outcomes validate that the proposed protocol is reliable, secure, and efficient in comparison with other existing state of art related user authentication protocols.

Kriging Regressive Pseudo Randomized Lamport Certificateless Signcryption based Replication Attack Detection for Secure Routing in WSN

A Wireless Sensor Network (WSN) includes small, low-cost, and resource-constrained sensor nodes to perform monitoring. Each node is free to move and susceptible to various attacks. Therefore, WSNs have huge attention to communication security. Identifying and monitoring attack node is complex in distributed network and creates node replication attacks in WSN. In order to improve secure routing, a novel technique called Kriging Regressive Attack Detection based Pseudo Randomized Lamport Certificateless Signcryptive Secure Routing (KRAD-PRLCSSR) is proposed. KRAD-PRLCSSR enhances the secure routing by detecting the replication attack in WSN. Initially, the neighbor discovery is performed using the Kriging Regression function. Energy of every sensor node is calculated and validates with threshold value. Based on estimated results, normal node or replica node is identified. Then the normal node is chosen as neighboring node. After that, secure transmission is performed using Pseudo Randomized Lamport one-time Generative Certificateless Signcryption. By applying the signcryption technique, Pseudo Randomized private and public keys are generated for each sensor node. After the key generation, the source node performs the encryption as well as a digital signature. The encrypted data and signature are sent to receiver. Finally, signature is verified at the receiver side to decrypt the data. This helps to increase the security of routing in WSN. The simulation analysis of KRAD-PRLCSSR technique is performed with the different metrics.

EDVWDD: Event-Driven Virtual Wheel-based Data Dissemination for Mobile Sink-Enabled Wireless Sensor Networks

Despite the several advantages of mobile sink-enabled wireless sensor networks, the data dissemination to mobile sink is challenging for resource-constrained sensor nodes due to sink mobility. Sensor nodes need to be aware of current location of the mobile sink to successfully transmit their data. However, network-wide updation of mobile sink’s location would cause high communication overhead and undermine the energy conservation goal. In this paper, we propose an event-driven virtual wheel-based data dissemination (EDVWDD) scheme that constructs multiple wheels in the sensor field with the aim of reducing the cost of updating the mobile sink location. Furthermore, EDVWDD provides easy-accessibility of wheel to each sensor node in order to reduce the delay while transmitting the data to mobile sink. Simulation results reveal improved performance of EDVWDD in terms of energy consumption, and data delivery delay as compared to state-of-the-art mechanisms at varying node density and sink speed.