Dynamic Wireless Sensor Networks Research Articles

Adaptive Location-based Routing Protocols for Dynamic Wireless Sensor Networks in Urban Cyber-physical Systems

This study investigates the development and enhancement of adaptive location-based routing protocols within dynamic wireless sensor networks (WSNs) in urban cyber-physical systems, recommending the implementation of the study’s innovative Urban Adaptive Location-based Routing Protocol (UALRP). This innovative protocol integrates real-time data analytics and adaptive machine learning models into its algorithmic framework to dynamically optimize routing decisions based on continuously changing urban conditions. Through the utilization of data-driven simulation models and machine learning techniques, the research sought to significantly improve the efficiency, reliability, and scalability of urban WSNs. Existing protocols such as Geographic Adaptive Fidelity (GAF), Greedy Perimeter Stateless Routing (GPSR), and Dynamic Source Routing (DSR) were critically assessed under urban settings using extensive datasets detailing New York City's traffic patterns and environmental variables. The analysis demonstrated that while GPSR showed superior performance in terms of latency, throughput, and energy efficiency among the traditional protocols, the introduction of UALRP, with its advanced predictive and adaptive capabilities, can further optimize these metrics. The study affirms the critical role of enhancing location accuracy and the ongoing advancement of machine learning models within urban routing protocols. These insights advocate for the broader implementation of adaptive strategies like UALRP to foster the development of more resilient and efficient urban cyber-physical systems.

Topological information embedded convolutional neural network-based lotus effect optimization for path improvisation of the mobile anchors in wireless sensor networks

ABSTRACT Wireless sensor networks (WSNs) rely on mobile anchor nodes (MANs) for network connectivity, data aggregation, and location information. However, MANs’ mobility can disrupt energy consumption and network performance. Effective path improvisation algorithms are needed for MANs to optimize energy use, reduce data loss, and maintain network connectivity in dynamic WSN environments. To overcome these issues, Topological Information Embedded Convolutional Neural Network based Lotus Effect Optimization for Path Improvisation of the Mobile Anchors in Wireless Sensor Networks (TIECNN-PIMA-OAC-WSN) was proposed. The approach establishes a robust network setup and energy model, employing TIECNN for initial cluster formation and cluster head selection. The chosen cluster head, termed the Mobile Anchor, undergoes optimization using the Lotus effect optimization algorithm to determine the most efficient and shortest path. This work enhances both the topological information processing and energy efficiency of mobile anchor paths. The simulation outcomes prove the proposed technique attains 33.12%, 39.56%, and 42% higher network lifespan for sensor nodes density 40; 38.22%, 29.66%, and 41.33% higher network lifespan for sensor nodes density 60; 37.45%, 35.55%, and 43.67% higher network lifespan for sensor nodes density 80; 32.45%, 29.45%, and 46.66% higher network lifespan for sensor nodes density 100 analysed to the existing methods.

A Deterministic Approach with Markov Chain Algorithm to Impose Higher Order Security in Dynamic Wireless Sensor Networks (DWSN)

A Deterministic Approach with Markov Chain Algorithm to Impose Higher Order Security in Dynamic Wireless Sensor Networks (DWSN)

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.

Flamingo Jelly Fish search optimization-based routing with deep-learning enabled energy prediction in WSN data communication

ABSTRACT Nowadays, wireless sensor networks (WSN) have gained huge attention worldwide due to their wide applications in different domains. The limited amount of energy resources is considered as the main limitations of WSN, which generally affect the network life time. Hence, a dynamic clustering and routing model is designed to resolve this issue. In this research work, a deep-learning model is employed for the prediction of energy and an optimization algorithmic technique is designed for the determination of optimal routes. Initially, the dynamic cluster WSN is simulated using energy, mobility, trust, and Link Life Time (LLT) models. The deep neuro-fuzzy network (DNFN) is utilized for the prediction of residual energy of nodes and the cluster workloads are dynamically balanced by the dynamic clustering of data using a fuzzy system. The designed Flamingo Jellyfish Search Optimization (FJSO) model is used for tuning the weights of the fuzzy system by considering different fitness parameters. Moreover, routing is performed using FJSO model which is used for the identification of optimal path to transmit data. In addition, the experimentation is done using MATLAB tool and the results proved that the designed FJSO model attained maximum of 0.657J energy, a minimum of 0.739 m distance, 0.649 s delay, 0.849 trust, and 0.885 Mbps throughput.

An energy-efficient command broadcast protocol in dynamic WSNs

In wireless sensor networks (WSNs), a sink must broadcast periodically a command message to a group of nodes or all nodes to control the operation of the network. However, the sparse distribution and limited resources of nodes bring difficulties for efficient broadcasting. In this paper, we propose an energy-efficient command broadcast protocol based on the construction of a spanning tree which includes maximizing the number of leaf nodes, and the allocation of one sharable slot to each tree level. The sharable slot that allocates to tree level will be divided into multiple mini slots, and non-leaf nodes at the same level will compete for broadcasting; in this way, collision can be reduced. Moreover, a node that moves to another level can use the sharable slot allocated to the new level without reallocating the slots. Extensive simulations prove that this approach not only achieves high reliability of message delivery but also energy efficiency.

English

This paper proposes a solution to enhance the key management of Wireless Sensor Networks (WSNs) by integrating blockchain technology with Hierarchical Identity-Based Encryption. The aim is to establish a highly secure WSN structure. The current wireless network is improved by incorporating blockchain technology to increase security and reliability. However, the security of key management in dynamic WSNs is threatened by the vulnerability of untrusted Head Nodes(HN) to attack. The presence of an untrusted HN imposes a heavy burden on the sensors for key management, in addition to the already existing challenges. In order to overcome these issues, blockchain technology has been used to propose a secure key management scheme (BSKM). We offer a secure node movement technique and a safe cluster-building mechanism, with the blockchain acting as a trust machine to replace the major functions of the Head node. We run extensive simulations for security analyses. The findings show that the BSKM scheme is efficient and effective and is more suitable for enhancing the reliability of dynamic WSNs.

Interleaved Honeypot-Framing Model with Secure MAC Policies for Wireless Sensor Networks

The Wireless Medium Access Control (WMAC) protocol functions by handling various data frames in order to forward them to neighbor sensor nodes. Under this circumstance, WMAC policies need secure data communication rules and intrusion detection procedures to safeguard the data from attackers. The existing secure Medium Access Control (MAC) policies provide expected and predictable practices against channel attackers. These security policies can be easily breached by any intelligent attacks or malicious actions. The proposed Wireless Interleaved Honeypot-Framing Model (WIHFM) newly implements distributed honeypot-based security mechanisms in each sensor node to act reactively against various attackers. The proposed WIHFM creates an optimal Wireless Sensor Network (WSN) channel model, Wireless Interleaved Honeypot Frames (WIHFs), secure hash-based random frame-interleaving principles, node-centric honeypot engines, and channel-covering techniques. Compared to various existing MAC security policies, the proposed model transforms unpredictable IHFs into legitimate frame sequences against channel attackers. Additionally, introducing WIHFs is a new-fangled approach for distributed WSNs. The successful development of the proposed WIHFM ensures resilient security standards and neighbor-based intrusion alert procedures for protecting MAC frames. Particularly, the proposed wireless honeypot methodology creates a novel idea of using honeypot frame traps against open wireless channel attacks. The development of a novel wireless honeypot traps deals with various challenges such as distributed honeypot management principles (node-centric honeypot, secretly interleaved-framing principles, and interleaving/de-interleaving procedures), dynamic network backbone management principles (On Demand Acyclic Connectivity model), and distributed attack isolation policies. This effort provides an effective wireless attack-trapping solution in dynamic WSNs. The simulation results show the advantage of the proposed WIHFM over the existing techniques such as Secure Zebra MAC (SZ-MAC), Blockchain-Assisted Secure-Routing Mechanism (BASR), and the Trust-Based Node Evaluation (TBNE) procedure. The experimental section confirms the proposed model attains a 10% to 14% superior performance compared to the existing techniques.

An optimized routing scheme for congestion avoidance using mobile nodes in Wireless Sensor Network

Wireless Sensor Network (WSN) consists of a group of dynamic sensor nodes such nodes form a volatile topology. When an event occurs the dynamic nodes WSN communicate wirelessly from source to destination node without any infrastructure. Dynamic sensor nodes are vital characteristics of WSN where link breakage occurs recurrently and change in topology. Due to this overall network Quality of Service (QoS) degrades due to link failure. Whenever an event in the network experience packet loss due to link failure which leads one of the causes for congestion. However predictable network tries to discover alternate path whenever the link failure occurs on demand which does not have the overall network information thus increasing the communication overhead, delay, and high energy consumption. To address these issues this problem paper proposes an optimized routing scheme known as low latency optimal link-state routing (LL-OLSR) for mobile in WSN which proactively monitors the established path and connects the source to destination with minimum cost with optimal energy. LL-OLSR algorithm finds the disjoint link where it is detected between two nodes and also maximizes the packet delivery ratio by minimizing path delay using connectivity range and queuing delay by which congestion is avoided. Simulation experiment of proposed LL-OLSR scheme is carried on event-driven simulation tool –NS2 and QoS parameters are evaluated by comparing with existing routing scheme.

Node Replication Attack Detection in Distributed Wireless Sensor Networks

Wireless sensor network (WSN) is an emerging technology used in emergency scenarios. There are a number of possible threats to WSNs because they use unsupervised IP addresses. Securing networks with unattended sensors is a real challenge nowadays. Sensor nodes lack power and storage, making them incompatible with normal security checks. It will be vital to make advancements in sensor network architecture and protocol design. There will be more vulnerability to attack if there is a lack of security. Especially, one key attack is node replication which induces the sensor node to acts as an original node, collecting data from the network and sending it to the attacker. In dynamic WSN, detecting an assault is difficult to find replica nodes. Therefore, this paper proposes a Strategic Security System (SSS) to discover replica nodes in static and dynamic distributed WSNs. It is mainly focused on enhancing detection accuracy, time delay, and communication overhead. The present system includes Single Stage Memory Random Walk with Network Division (SSRWND) and a Random-walk-based approach to detect clone attacks (RAWL). The proposed system has less memory and better detection accuracy.

Range-Based Relative Navigation for a Swarm of Centimeter-Scale Femto-Spacecraft

In-orbit relative navigation between a networked swarm of centimeter-scale femto-spacecraft would add considerable value to a range of space mission concepts and applications, such as for multipoint sensing and distributed sparse aperture interferometry. For a swarm of networked femto-spacecraft, relative position determination would be possible by inferring coarse range estimates from the received signal strength indication associated with the communication link between swarm members. This is particularly advantageous for highly resource-constrained devices. In this paper, algorithms for swarm relative positioning using interspacecraft range estimates are presented that can be applied to centralized, decentralized, and distributed network configurations. Relative navigation filters for initial relative orbit determination and state estimation are presented for femto-spacecraft swarm deployment and dispersal scenarios. The algorithms presented could also find use in terrestrial applications, in static and dynamic wireless sensor networks.

IPSMT: Multi-objective optimization of multipath transmission strategy based on improved immune particle swarm algorithm in wireless sensor networks

Multipath transmission routing in dynamic wireless sensor network deployed with movable nodes is a multi-objective optimization issue with multi-parameter constraints. Traditional strategies have the deficiencies such as high computational complexity, long solving time, difficulty in obtaining optimal value and easy to fall into local solution especially in the scenario of large quantity of movable sensor nodes being deployed. Therefore, an improved immune particle swarm optimization based multipath transmission strategy (IPSMT) is presented. It contains three parts: improved immune particle swarm algorithm (IIPSO), IPSMT and fault tolerance strategy in multipath transmission routing (FTMT). IIPSO aims to improve the diversity of particle population and accelerate the convergence of multipath establishment respectively through concentration regulation mechanism and immune vaccine operation improvement. IPSMT and MTFT is to establish and optimize multiple transmission paths by considering the movable nodes’ moving distance and energy consumption quality by multi-objective optimization method. Network fault tolerance model has been established to analyze the algorithm’s convergence, energy consumption balance, network fault tolerance and time complexity. Through the multi-objective optimization simulation and analysis of multipath establishment comparing with the related works, IPSMT shows good global searching ability and convergence performance as well as diversity of solution population to realize the optimization of multipath transmission routing. The whole network is proved to have good performance of transmission stability and fault tolerance.

Enhancing QoS and Residual Energy by Using of Grid-Size Clustering, K-Means, and TSP Algorithms With MDC in LEACH Protocol

Some recent researches have shown that the energy consumption problem caused by data collection in a wireless sensor network (WSN) based on a static data collector is a main threat to the network lifetime. However, with the progress of the mobile terminal technology, the implementation of mobile data collectors (MDCs) has become more popular in large-scale WSNs, but it remains a big problem to improve the Quality of Service (QoS) criteria and minimize the energy consumption at the same time. However, most existing systems based on MDCs do not successfully strike a balance between routing energy consumption and QoS. In addition, most WSN protocols fail to maintain their impact when the network topology changes. Thus, for a dynamic WSN, it is important to support an intelligent MDC to continue data propagation despite the inevitable changes in the WSN topology. Considering all the above challenges, we propose a new intelligent MDC based on the traveling salesman problem (TSP) to determine the optimal path traveled by the MDC for energy efficiency and latency. Specifically, our proposed Mobile Data Collectors-Traveling Salesman Problem-Low Energy Adaptive Clustering Hierarchy-K-Means (MDC-TSP-LEACH-K) protocol uses K-Means and Grid clustering algorithm to decrease energy consumption in the cluster head (CH) election phase. Additionally, MDC is utilized as an intermediate between CH and the sink to further enhance the QoS of WSNs, to reduce delays while collecting data, and improve the transmission phase of the LEACH protocol.

DMTC: Optimize Energy Consumption in Dynamic Wireless Sensor Network Based on Fog Computing and Fuzzy Multiple Attribute Decision-Making

Advances in wireless technologies and small computing devices, wireless sensor networks can be superior technology in many applications. Energy supply constraints are one of the most critical measures because they limit the operation of the sensor network; therefore, the optimal use of node energy has always been one of the biggest challenges in wireless sensor networks. Moreover, due to the limited lifespan of nodes in WSN and energy management, increasing network life is one of the most critical challenges in WSN. In this investigation, two computational distributions are presented for a dynamic wireless sensor network; in this fog-based system, computing load was distributed using the optimistic and blind method between fog networks. The presented method with the main four steps is called Distribution-Map-Transfer-Combination (DMTC) method. Also, Fuzzy Multiple Attribute Decision-Making (Fuzzy MADM) is used for clustering and routing network based on the presented distribution methods. Results show that the optimistic method outperformed the blind one and reduced energy consumption, especially in extensive networks; however, in small WSNs, the blind scheme resulted in an energy efficiency network. Furthermore, network growth leads optimistic WSN to save higher energy in comparison with blinded ones. Based on the results of complexity analysis, the presented optimal and blind methods are improved by 28% and 48%, respectively.

An adaptive hierarchical data dissemination mechanism for mobile data collector enabled dynamic wireless sensor network

Recent studies have revealed that the energy hole problem caused by multi-hop data collection in a static data collector based wireless sensor network(WSN) is a major threat to the lifetime of the network. With the advancement of mobile terminal technology, the application of mobile data collectors (MDCs) has gained popularity in a large scale WSN. A WSN design with one or more MDC can significantly diminish the energy hole problem, which in turn prolongs the network operational lifetime. However, most of the MDC based existing schemes suffer from achieving a balance between routing energy consumption and data delivery delay. Moreover, the majority of the sensor network protocols fail to retain their impact as the network topology changes due to the inclusion or exclusion of nodes. So, for a dynamic WSN, it is essential to support a recovery mechanism to continue the data propagation despite inevitable changes in the WSN topology. Considering all the aforementioned challenges, this paper proposes an adaptive hierarchical data dissemination (AHDD) mechanism for multiple MDC enabled dynamic wireless sensor network, which alleviates the energy hole problem as well as resolves the fault-tolerance issue. Simulation results demonstrate that the proposed protocol has shown improved performance with respect to performance metrics namely, network lifetime, energy efficiency, packet delivery percentage, and end-to-end delay.

Optimized localization using naked mole‐rat algorithm in dynamic wireless sensor networks

SummaryLocalization in wireless sensor networks (WSNs) becomes an essential and important part of research in past few years. Finding location of the occurring event is essential in most of the applications, namely, monitoring of objects, tracking of animals and various resources placed in the indoor and outdoor environments. WSN consists of sensors placed in huge amounts across large regions of globe to perform various tasks such as data sensing and communicating with other devices. The aim of this research is to analyse various location optimization techniques to address a WSN localization problem and assigning appropriate coordinates to the unknown sensor nodes. In this paper, the authors have introduced the concept of sensor node localization in a 2D dynamic environment using a single anchor node (static) along with virtual anchors to locate target nodes (dynamic) using hexagonal projection technique, and further, the estimated target node coordinates are optimized using novel naked mole‐rat algorithm (NMRA). This paper discusses the performance of various location optimization techniques available in literature with NMRA‐based technique in terms of localization error and number of localized nodes.

Data tampering attacks diagnosis in dynamic wireless sensor networks

Real-world wireless sensor networks (WSNs) usually have dynamic topologies and are vulnerable to data tampering attacks, which may possibly lead to serious consequences. This paper addresses the issue of defending against data tampering attacks to dynamic WSNs. First, a hybrid diagnosis algorithm is proposed, which consists of three phases: data comparison phase, distributed diagnosis phase, and global diagnosis phase. In the distributed diagnosis phase, a majority voting mechanism and a multi-round diagnosis mechanism are introduced. As the phase may lead to inconsistent diagnosis results among different sensor nodes, we introduce a global diagnosis phase to overcome the shortcoming. Next, we evaluate the performance of the diagnosis algorithm, accompanied with a few examples. Finally, to achieve near perfect performance, some experiments on how to choose the parameters of the algorithm are provided. This work provides guidance for dealing with data tampering attacks in dynamic WSNs.

Stable routing and energy-conserved data transmission over wireless sensor networks

Stable routing and energy conservation over a wireless sensor network (WSN) is a major issue in Internet of Things applications. The network lifetime can be increased when studying this issue with interest. Data transmission is a dominant factor in IoT networks for communication overhead and energy consumption. A proposed efficient node stable routing (ENSR) protocol is introduced to guarantee the stability of transmission data between the source and destination nodes, in a dynamic WSN conditions. ENSR minimizes energy consumption and selects more stable nodes for packets forwarding. Stability becomes the most important factor that qualifies the node's centrality. A node’s stability is characterized by residual energy, link quality, and number of hops needed to reach the destination from the node. To calculate node's stability, an enhanced centrality concept, known as stable betweenness centrality (SBC) is introduced. In ENSR, at first, some nodes will be selected as the stable forwarding nodes, usually with maximum SBC between their neighbors within a limited communication radio range of a particular region. Furthermore, each stable forwarding node then broadcasts its identity, including SBC, to the source node separately. The source node can compute a stable path to forward packets to the corresponding stable forwarding node, based on a proper designed stable path routing metric (SPRM). Then, the stable forwarding node will behave as a new source node and start another stable path routing process until the packets are forwarded and reached to the destination node. In addition, the change of stable nodes over time balances and conserves node energy consumption, thereby mitigating “hot spots”. The proposed routing protocol is validated through simulation. The numerical results show that the proposed protocol outperforms the existing algorithms, global and local reliability-based routing (GLRR) and reliable energy-aware routing protocol (RER), in terms of network efficiency and reliability.

Fuzzy Based Unequal Clustering and Context-Aware Routing Based on Glow-Worm Swarm Optimization in Wireless Sensor Networks: Forest Fire Detection

Wireless sensor networks (WSNs) displays an encouraging outcome for forest fire (FF) identification. The most serious WSN investigation tasks is forest fire’s early prediction, which is used to save the ecosystem. For conveying the detected information to the BS (base station), the SNs (sensor nodes) are placed in remote forest region in WSN based FF discovery scheme that is manageable by the forest sector. Various studies have been finished in this field but they studied only few amount of constraints and the encountered situations influence has not discussed after the system positioning. In this work, fuzzy based unequal clustering and context aware routing (CAR) procedure with GSO (glow-worm swarm optimization) is developed in RWP (random way point) based dynamic WSNs. Based on FL (fuzzy logic) the unequal clustering is formed and the optimal CH (cluster head) is nominated to convey the information from CM (cluster member) to BS to increase the system lifespan and to decrease the energy consumption. Further, the routing process is performed by the CAR procedure with GSO process to enhance the efficiency of network. Lastly, a case study of FF identification is offered as a justification of the suggested method. The suggested work is executed in MATLAB. The simulation outcomes proved that the proposed approach provide the better outcomes in average energy consumption (0.025 J), PDR (99.4%), jitter (4.01 s), delay (0.0304 s), BER (15%), throughput (144.6Kbps), network lifetime (38.7 s) as related to other current protocols.

An adaptive multilevel location based key management system for dynamic wireless sensor networks

ABSTRACT A Key management system plays an important role in the process of wireless communication between the nodes of a Wireless Sensor Network (WSN). Unlike the wired networks, WSNs are more vulnerable to attacks from the malicious nodes. To overcome the shortcomings of the existing key management systems, this paper proposes an adaptive multilevel location-based key management system (AML-KBS), in which the keys are generated dynamically and shared among the nodes of the wireless networks. Since the proposed approach follows a location-based system for key management, the attackers can be differentiated based upon their location. Also, the proposed system has proven its withstanding against node capture attacks. Comparing with the existing approaches, the memory requirement of the proposed system has shown better improvement. Moreover, the proposed methodology provides better security mechanism than the existing key management systems.