Active Nodes Research Articles

An optimized approach to enhance the network lifetime through integrated data aggregation and data dissemination in wireless sensor network

<div class="WordSection1"><p>Wireless sensor network (WSN) is an integral part of internet of things (IoT), it comprises multiple sensor nodes to sense that data for various applications; also, sensor nodes have limited energy. Hence, several researchers to improvise the network lifetime and reduce energy through machine learning approaches like clustering have used data aggregation; considering the WSN architecture and development of novel use cases and dynamic behavior, data aggregation cannot solve the problem of efficiency solely. Hence integrating data aggregation and data dissemination can provide a research scope to achieve optimal efficiency. This research work introduces an integrated-data aggregation and data dissemination (DADD) to develop an efficient WSN-based model for lifetime enhancement. Integrated-DADD follows two sub-mechanisms; the first part of the mechanism introduces an optimal clustering technique to perform the clustering and optimal parameter tuning is formulated and efficient data aggregation takes place. The second part of the integrated-DADD introduces optimal data dissemination through optimal path selection, which helps in finding the suitable path for data dissemination. Integrated-DADD is evaluated considering the parameters like energy consumption, network lifetime in terms of rounds; active node participation, and communication overhead, comparative analysis indicates that integrated-DADD outperforms the existing model.</p></div>

Performance Analysis of Random Access NOMA for Critical mIoT With Timer-Power Back-Off Strategy

Massive machine type communication (mMTC) and Internet-of-Things (IoT) networks provide global connectivity for massive number of end devices anytime-anywhere. The most challenging part in implementing such networks is the development of spectrum access strategies to provide ultra-reliable low-latency (URLL) transmissions for a large number of nodes with sporadic traffic behavior. Such networks have to deploy spectrum-efficient transmission schemes, and thus, non-orthogonal multiple-access (NOMA) is considered as a viable solution, that can be used to provide high number of URLL transmissions. We propose a random access NOMA transmission protocol (RA-NOMA) for IoT networks with large number of clustered IoT devices is proposed. The nodes in the proposed scheme adopt timer and power back-off strategies to transmit their short packets in a collision-free NOMA-based manner to achieve the URLL requirements. Closed-form expressions for network metrics, namely, delay violation probability, average packet latency, reliability, and effective sum rate (ESR) are analytically derived. Furthermore, the effect of blocklength, back-off timer (countdown value) and the number of active nodes on network metrics is explored. Additionally, the effect of the estimation error on the number of active nodes is analyzed and the impact of preamble transmit power on reliability in the presence of estimation error is investigated. Moreover, mathematical expressions for network metrics are also derived for NOMA-ALOHA with transmission diversity (NOMA-ALOHA-TD) in the underlying scenario, and the obtained results from NOMA-ALOHA-TD and the proposed RA-NOMA are compared.

An Efficient Routing Awareness Based Scheduling Approach in Energy Harvesting Wireless Sensor Networks

Wireless sensor networks (WSNs) have emerged as a promising technology for various Internet of Things (IoT)-based sensing applications. Generally, sensor nodes are powered by non-rechargeable and low-capacity batteries; they only last for a small amount of time. Due to their dispersed nature, sensor nodes cannot have their batteries replaced or maintained locally. The energy harvesting (EH) concept shows commitment as a method for addressing the current energy shortage. Another problem is the intra-energy control of various network load functions, including coverage preservation, routing, clustering, etc. This study proposes an ERAS (efficient routing awareness scheduling) algorithm to address the abovementioned issues. The ERAS method employs a solar harvesting system as a hierarchical clustering-based routing protocol. The presented scheme dynamically alters nodes between states and adjusts to new states based on the insides of sensed data packets. Synchronization-based scheduling ensures proper coordination between the cluster head (CH) and cluster members (CMs). It selects the CH based on both remaining and harvested energy and the number of active nodes to improve throughput. Surprisingly proposed EASR algorithm performs efficiently while considering various performance matrices, including network lifetime, packet delivery ratio, energy consumption, and network sustainability. Extensive simulation results exhibit the network lifetime of the proposed ERAS technique (79.8%) compared to ERA, SEED and CREST methods as 67.3%, 73.8%, and 75.2%, respectively.

Group Sparse Space Information Network With Joint Virtual Network Function Deployment and Maximum Flow Routing Strategy

For the space information network (SIN) with network function virtualization (NFV), a large number of active nodes deployed with virtual network functions (VNFs) impose heavy coordination overhead. In this paper, we investigate the trade-off between the network maximum flow and coordination overhead under the service function chain (SFC) constraints. Specifically, we propose the group sparse joint VNFs deployment and flow routing strategy (GS-VNF-R) to strike the trade-off between the network maximum flow and coordination overhead. Although the GS-VNF-R scheme can be formulated as a convex problem, for a large-scale SIN, solving the GS-VNF-R problem by traditional convex optimizations imposes a heavy computation burden. In order to reduce the time complexity, we propose a novel optimal low-complexity block-successive upper-bound minimization method of multipliers based group sparse (BSUM-M-GS) algorithm, which can converge to the global optimal with much less complexity. Simulation results show that for some scenarios, 60% of active nodes can be saved by using the proposed GS-VNF-R scheme without any performance loss compared to the full cooperation scheme, which results in significant cooperation overhead reduction. Moreover, simulation results demonstrate that our proposed BSUM-M-GS algorithm can significantly reduce the complexity to the extent of 7 orders of magnitude for some scenarios.

Investigating structural and functional aspects of the brain’s criticality in stroke

This paper addresses the question of the brain’s critical dynamics after an injury such as a stroke. It is hypothesized that the healthy brain operates near a phase transition (critical point), which provides optimal conditions for information transmission and responses to inputs. If structural damage could cause the critical point to disappear and thus make self-organized criticality unachievable, it would offer the theoretical explanation for the post-stroke impairment of brain function. In our contribution, however, we demonstrate using network models of the brain, that the dynamics remain critical even after a stroke. In cases where the average size of the second-largest cluster of active nodes, which is one of the commonly used indicators of criticality, shows an anomalous behavior, it results from the loss of integrity of the network, quantifiable within graph theory, and not from genuine non-critical dynamics. We propose a new simple model of an artificial stroke that explains this anomaly. The proposed interpretation of the results is confirmed by an analysis of real connectomes acquired from post-stroke patients and a control group. The results presented refer to neurobiological data; however, the conclusions reached apply to a broad class of complex systems that admit a critical state.

The Partition Bridge (PB) tree: Efficient nearest neighbor query processing on road networks

With the rapid development of location-based services, many types of applications in urban areas such as transportation planning, traffic management, and deployment of infrastructure depend on spatial objects that are distributed along the road network. As current place datasets include millions of spatial objects and human mobility datasets capture billions of locations, it is an important challenge to answer k-nearest neighbor queries efficiently. However, shortest-path distance calculations are the computational bottleneck for a k-nearest neighbor queries on road networks. Existing query algorithms partition the network to mitigate this bottleneck, but they do not take the data distribution into account, which leads to inefficiencies in dense areas and sparse areas. In this paper, an efficient and scalable indexing method called the Partition Bridge tree (PB-tree), is proposed based on hierarchical network partitions that consider network connectivity and data distribution. The structure of the PB-tree mainly includes distance matrices, union-bridges, bridges, and active network nodes component. Based on the PB-tree, a k nearest neighbor query processing algorithm is proposed by combining “bottom-up”, “top-down”, and adjacent extension methods. By using different road networks and datasets, the effectiveness and practicability of PB-tree are evaluated; the experimental results show that PB-tree outperforms the state-of-the-art methods and the classical approach.

Identifying top influential spreaders based on the influence weight of layers in multiplex networks

Detecting influential nodes in multiplex networks is a complex task due to the presence of multiple layers. In this study, we propose a method for identifying important layers with strong spreaders based on several key parameters. These include a layer's position within a well-connected neighborhood, the number of active edges and critical nodes, the ratio of active nodes to all possible connections, and the intersection of intra-layer communication compared to other layers. To accomplish this, we have formulated a layer weighting method which takes into account these parameters, and developed an algorithm for mapping and computing the rank of nodes based on their spreading capability within multiplex networks. The resulting layer weighting is then used to map and compress centrality vector values to a scalar value, allowing us to calculate node centrality in multiplex networks via a coupled set of equations. Moreover, our method combines the important layer parameters to compute the influence of nodes from different layers. Our experimental results, conducted on both synthetic and real-world networks, demonstrate that the proposed approach significantly outperforms existing methods in detecting high influential spreaders. These findings highlight the importance of using a suitable layer weighting measure for identifying potential spreaders in multiplex networks.

Epidemic spreading on multi-layer networks with active nodes.

Investigations on spreading dynamics based on complex networks have received widespread attention these years due to the COVID-19 epidemic, which are conducive to corresponding prevention policies. As for the COVID-19 epidemic itself, the latent time and mobile crowds are two important and inescapable factors that contribute to the significant prevalence. Focusing on these two factors, this paper systematically investigates the epidemic spreading in multiple spaces with mobile crowds. Specifically, we propose a SEIS (Susceptible-Exposed-Infected-Susceptible) model that considers the latent time based on a multi-layer network with active nodes which indicate the mobile crowds. The steady-state equations and epidemic threshold of the SEIS model are deduced and discussed. And by comprehensively discussing the key model parameters, we find that (1) due to the latent time, there is a "cumulative effect" on the infected, leading to the "peaks" or "shoulders" of the curves of the infected individuals, and the system can switch among three states with the relative parameter combinations changing; (2) the minimal mobile crowds can also cause the significant prevalence of the epidemic at the steady state, which is suggested by the zero-point phase change in the proportional curves of infected individuals. These results can provide a theoretical basis for formulating epidemic prevention policies.

Dynamic Optimization of Random Access in Deadline-Constrained Broadcasting

This paper considers dynamic optimization of random access in deadline-constrained broadcasting with frame-synchronized traffic. Under the non-retransmission setting, we define a dynamic control scheme that allows each active node to determine the transmission probability based on the local knowledge of current delivery urgency and contention intensity (i.e., the number of active nodes). For an idealized environment where the contention intensity is completely known, we develop a Markov Decision Process (MDP) framework, by which an optimal scheme for maximizing the timely delivery ratio (TDR) can be explicitly obtained. For a realistic environment where the contention intensity is incompletely known, we develop a Partially Observable MDP (POMDP) framework, by which an optimal scheme can only in theory be found. To overcome the infeasibility in obtaining an optimal or near-optimal scheme from the POMDP framework, we investigate the behaviors of the optimal scheme for extreme cases in the MDP framework, and leverage intuition gained from these behaviors together with an approximation on the contention intensity knowledge to propose a heuristic scheme for the realistic environment with TDR close to the maximum TDR in the idealized environment. We further generalize the heuristic scheme to support retransmissions. Numerical results are provided to validate our study.

Radioisotope-Guided Excision of Mediastinal Lymph Nodes in Patients with Non-Small Cell Lung Carcinoma: Feasibility and Clinical Impact.

Intraoperative localisation of nodal disease in non-small cell lung cancer (NSCLC) can be challenging. Lymph node localisation via radiopharmaceuticals is used in many conditions; we tested the feasibility of this approach in NSCLC. NSCLC patients were prospectively recruited. Intraoperative peri-tumoral injections of [99mTc]Tc-albumin nanocolloids were performed, followed by removing the tumour and locoregional lymph nodes. These were examined ex vivo with a gamma probe and labelled sentinel lymph nodes (SLNs) if they showed any activity or non-sentinel lymph nodes (nSLNs) if they did not. Thereafter, the surgical field was scanned with the probe; any further radioactive lymph node was removed and labelled as "extra" SLNs (eSLNs). All specimens were sent to histology, and metastatic status was recorded. 48 patients were enrolled, and 290 nodal stations were identified: 179 SLNs, 87 nSLNs, and 24 eSLNs. A total of 44 nodal metastases were identified in 22 patients, with 36 of them (82%) located within SLNs. Patients with nSLNs metastases had at least a co-existing positive SLN. No metastases were found in eSLNs. The technique shows high sensitivity for intraoperative nodal metastases identification. This information could allow selective lymphadenectomies in low-risk patients or more aggressive approaches in high-risk patients.

GPU-based mesh reduction strategy utilizing active nodes for structural topology optimization

This paper presents a novel mesh reduction strategy on graphics processing unit (GPU) that aims to reduce the computational complexity of conventional structural topology optimization. In the proposed strategy, the effective number of design variables is reduced by using the concept of active nodes and active elements in the finite element mesh. A novel mesh numbering scheme is also introduced to facilitate parallel identification of active nodes using a proposed GPU-based algorithm. The preconditioned conjugate gradient (PCG) solver is further developed using the proposed strategy and the numbering scheme. The proposed strategy is tested on three structural topology optimization problems using solid isotropic material with penalization method. Results of the proposed strategy demonstrate up to 8× speedup over the standard GPU implementation considering the entire mesh.

Efficacy of surgical comprehensive therapy for 456 cases of hypopharyngeal carcinoma

Objective: To retrospectively analyse the efficacy of surgerical comprehensive treatment for hypopharyngeal cancer. Methods: Four hundred and fifty-six cases of hypopharyngeal squamous cell carcinoma treated from Jan 2014 to Dec 2019 were analyzed retrospectively, including 432 males and 24 females, aged 37-82 years old. There were 328 cases of pyriform sinus carcinoma, 88 cases of posterior pharyngeal wall carcinoma, and 40 cases of postcricoid carcinoma. According to American Joint Committe on Cancer(AJCC) 2018 criteria, 420 cases were of stage Ⅲ or Ⅳ; 325 cases were of T3 or T4 stage. Treatment methods included surgery alone in 84 cases, preoperative planned radiotherapy plus surgery in 49 cases, surgery plus adjuvant radiotherapy or concurrent chemoradiotherapy in 314 cases, and inductive chemotherapy plus surgery and adjuvant radiotherapy in 9 cases. The primary tumor resection methods included transoral laser surgery in 5 cases, partial laryngopharyngectomy in 74 cases, of them 48 cases (64.9%) presented with supracricoid hemilaryngopharyngectomy, total laryngectomy with patial pharyngectomy in 90 cases, total laryngopharyngectomy or with cervical esophagectomy in 226 cases, and total laryngopharyngectomy with total esophagectomy in 61 cases. Among 456 cases, 226 cases received reconstruction surgery with free jejunum transplantation, 61 cases with gastric pull-up, and 32 cases with pectoralis myocutaneous flaps. All patients underwent retropharyngeal lymph node dissection, and high-definition gastroscopy was performed during admission and follow-up. SPSS 24.0 software was used to analyze the data. Results: The 3-year and 5-year overall survival rates were respectively 59.8%, and 49.5%. The 3-year and 5-year disease specific survival rates were respectively 69.0% and 58.8%. Total metastasis rate of retropharyngeal lymph nodes was 12.7%. A total of 132 patients (28.9%) suffered from simultaneous and metachronous multiple primary carcinoma of the hypopharynx. Multivariate Logistic regression analysis showed that T3-4 disease, cervical lymph node metastasis, retropharyngeal lymph node metastasis and postoperative adjuvant radiotherapy were independent factors affecting the prognosis of patients (all P<0.05). As of April 30, 2022, a total of 221 patients died during follow-up, of 109 (49.3%) with distant metastases, which were the main cause of death. Conclusions: The efficacy of comprehensive treatment for hypopharyngeal cancer can be improved by accurate preoperative evaluation, improved surgical resection, active retropharyngeal lymph node dissection and full process intervention of the second primary cancer.

Collaborative Intelligent Confident Information Coverage Node Sleep Scheduling for 6G-Empowered Green IoT

The Internet of Things (IoT) enabled by 6G increases the number of devices and users exponentially. 6G IoT will cover the overall domain of the world. Due to the limited resources in the IoT environment, greener and smarter networks are indispensable for the sustainable development of the 6G IoT. In this paper, the issue of improving resource efficiency and extending network lifetime is studied. We propose a novel Confidence Information Coverage (CIC) node sleep scheduling algorithm based on reinforcement learning (CICRL). In CICRL, collaborative intelligence is achieved through Q-learning to meet the coverage rate with the least active nodes, thus balancing the energy consumption and prolonging the network lifetime. Compared with the Coordination Algorithm based on Reinforcement Learning (COORD), Low-Energy Adaptive Clustering Hierarchy (LEACH) and Sleep Scheduling Approach based on Learning Automata (PCLA), the simulation results demonstrate that the proposed algorithm satisfies coverage with fewer active nodes and improves the network lifetime substantially.

Hypermetabolic Axillary Lymph Nodes Associated with COVID-19 Vaccination in Breast Cancer Management.

A 42-year-old female patient diagnosed with invasive ductal breast ca underwent 18F-fluorodeoxyglucose (FDG) positron emission tomography/ computed tomography (PET/CT) scan for staging, 1.5 cm diameter hypermetabolic lesion was observed in the lower inner quadrant of the right breast that was compatible with primary tumor [maximum standardized uptake value (SUVmax): 10.5]. No pathological 18F-FDG uptake was observed in lymph nodes whose fatty hilum was seen in the right axilla. However, in the left axilla and left deep axilla, hypermetabolic lymph nodes with a maksimum diameter of 19 mm and fatty hilum were observed (SUVmax: 8.0). In a detailed CT evaluation, these lymph nodes have thicker walls than the ones in the right axilla. The patient was questioned again and coronavirus disease-2019 (COVID-19) vaccination history (with BNT162b2, COVID-19 mRNA vaccine) was determined that was administrated to the left arm 5 days ago. Tru-cut biopsy was performed from the left aksillary lymph nodes and proved to be reactive lymphoid tissue and there was no primary or metastatic tumor in these axillary lymph node tissues. The patient was given neoadjuvant chemotherapy 4.5 months after the first 18F-FDG PET/CT, and the second was performed for the treatment response evaluation. Significant regression was determined from the findings. The patient underwent right total mastechtomy. She was being followed up with adjuvant chemotherapy and radiotherapy. In conclusion, hypermetabolic lymph nodes in the axillas should be interrogated for vaccination in patients with breast cancer. Hypermetabolic lymph nodes observed on the same side of the vaccinated arm in the 18F-FDG PET/CT scan may be related to vaccine-induced reactive lymph node enlargement. Lymph node metastasis may be excluded, especially if there are hypermetabolic lymph nodes with preserved fatty hilum in the contralateral axilla on the same side as the vaccinated arm. Active lymph nodes reactive to the vaccine become inactive after a while.

Crisis Assessment Oriented Influence Maximization in Social Networks

Influence maximization (IM) aims to find a subset of k nodes that can maximize the final active node set under an information diffusion model. With the development and popularity of social networks, the IM problem plays an essential role in various applications, such as public opinion analysis, viral marketing, and rumor early warning. However, most of the existing IM solutions have not accessed the risk of negative information from nodes in the future. In reality, a company may face economic loss when negative information breaks out of its spokesman. Therefore, a crisis assessment oriented and topic-based IM problem (TIM-CA) is proposed, which is utilized to model the IM problem by considering the crisis assessment (CA) and topics of users. To solve this problem, we propose a maximum influence arborescence model-based algorithm for TIM-CA, namely, MIA-TIM-CA. The proposed algorithm consists of crisis degree calculation, topic relevance calculation, and influence spread evaluation. More importantly, as for crisis degree calculation, it considers self-, topology-, and topic-based crisis degrees for each node. At the influence spread evaluation stage, MIA-TIM-CA proposes two functions to evaluate the node's importance and node influence spread. Extensive experiments on two real-world social networks demonstrate that our MIA-TIM-CA outperforms all comparison algorithms on influence spread, crisis score, and running time.

Adaptive Density Control Based on Random Sensing Range for Energy Efficiency in IoT Sensor Networks

IoT sensor networks enable long-term environmental monitoring. Most environmental applications require sensor node data gathering to satisfy application objectives. Therefore, sensing range optimization is a significant element in prolonging the lifetime of IoT sensor networks and saving energy. This study proposes an adaptive density control based on random sensing range (ADCR). It can reduce data redundancy by selecting several active and hybrid nodes in a sensing field. Thus, reducing redundancy power consumption will maximize the network lifetime. The simulation results demonstrate the effectiveness of density control based on the random sensing range.

Hypoxic tumor-derived exosomal miR-21 induces cancer-associated fibroblast activation to promote head and neck squamous cell carcinoma metastasis

BackgroundBoth microRNA-21-5p (miR-21) and the tumor microenvironment, including hypoxia and cancer-associated fibroblasts (CAFs), play a vital role in head and neck squamous cell carcinoma (HNSCC), but whether there is an interaction and the specific regulatory mechanism between them in the process of metastasis is still unclear. In this study, we aimed to elucidate the connection and regulatory mechanism of miR-21, hypoxia, and CAFs in HNSCC metastasis. MethodsThe underlying mechanisms of hypoxia inducible factor 1 subunit alpha (HIF1α) regulating miR-21 transcription, promoting exosome secretion, CAFs activation, tumor invasion, and lymph node metastasis were determined through quantitative real-time PCR, immunoblotting, transwell, wound healing, immunofluorescence, ChIP, electron microscopy, nanoparticle tracking analysis, dual-luciferase reporter assay, co-culture model and xenografts experiments. ResultsMiR-21 promoted the invasion and metastasis of HNSCC in vitro and in vivo, whereas HIF1α knockdown inhibited these processes. HIF1α upregulated transcription of miR-21 and promoted the release of exosomes from HNSCC cells. Exosomes derived from hypoxic tumor cells were rich in miR-21, which induced NFs activation towards CAFs by targeting YOD1. Knockdown the expression level of miR-21 in CAFs prevented lymph node metastasis in HNSCC. ConclusionHypoxic tumor cell-derived exosomal miR-21 might be a therapeutic target to prevent or delay HNSCC invasion and metastasis.

A fuzzy/possibility approach for area coverage in wireless sensor networks

The literature approaches, devoted to sensor improve network coverage, are deterministic in terms of deployment environment and node configuration parameters. Nevertheless, this type of approaches has not proven to be very successful in uncertain deployment environments. This paper aims to deal with this issue using theories of uncertainty. We consider deployment environment’s imperfections and the characteristics of the sensor nodes. The selection of a minimum number of nodes for a minimum number of clusters to guarantee coverage in wireless sensor networks (WSNs) is uncertain. As a consequence, this paper proposes a hybrid Fuzzy-Possibilistic model to Schedule the Active/Passive State of Sensor nodes Strategy (FP-3SNS). This model helps to plan the scheduling of node states (Active/Passive) based on possibilistic information fusion to make a possibilistic decision for the node activation at each period. We evaluated our model (FP-3SNS) with (a) a running example (that shows the best choice of the active node with a probability of 0.81215); (b) a statistical evaluation (calculation of the confidence interface), where the average coverage reliability at 95% of FP-3SNS use is between (92.94, 96.27); and (c) a comparison with maximum sensing coverage region problem (MSCR), coverage maximization with sleep scheduling (CMSS), Spider Canvas Strategy, Semi-Random Deployment Strategy (SRDP), Probing Environment and Adaptive Sleeping with Location Information Protocol (PEAS-LI), and Variable Length Particle Swarm Optimization Algorithm with a Weighted Sum Fitness Function (WS-VLPSO). The simulation results highlight the benefits of using the fuzzy and possibility theories for treating the area coverage problem and the proposed model maintained a coverage between 99.99 and 90.00% for a significant period of time.

Robust function deployment against uncertain recovery time in different protection types with workload-dependent failure probability

Modern network services and functions, such as firewalls, load balancers, and network address translators, are virtualized to virtual network functions (VNFs) and run in virtual machines and containers. This paper proposes a robust VNF placement model against uncertain recovery time with satisfying an expected recovery time guarantee in a cost-efficient manner. We consider that each node fails with a workload-dependent failure probability, which is a non-decreasing function that reveals the empirical relationship between the workload and the failure probability. The preventively deployed backup resources can recover an unavailable function hosted by a failed node in a period of time, which is related to the backup strategies and protection types. Multiple functions protected by a node can share the backup resources to reduce the number of active nodes to save cost, which also affects the recovery time if the number of unavailable functions is so large that the remaining capacity cannot recover them and causes a waiting procedure of an unavailable function before being recovered. We introduce an uncertainty set that considers the upper and lower bounds of the recovery time of a function protected by each node and the upper bound of the average recovery time among nodes. We consider the expected remaining capacity of a node and the expected number of unavailable functions hosted by the node to approximate the waiting time in the shared protection. The robust optimization technique is applied to obtain the worst-case expected recovery time under an uncertain recovery time set. With this technique, the model is formulated as a mixed integer linear programming problem. To solve the problem in a practical time, a heuristic algorithm is developed. It reduces the number of active nodes while decreasing the worst-case expected recovery time within the uncertainty set by converting the linear programming problem to a graph problem. The numerical results reveal the superiority of the proposed model by considering the recovery time guarantee, uncertainty set, and shared protection; we investigate the dependency on the bounds of recovery time.

A Nordic Colonial Career Across Borders: Hjalmar Björling in the Dutch East Indies and China

ABSTRACT Building on the latest literature on transimperial mobilities, this article analyses the career of the Finland-Swedish Hjalmar Björling, a merchant and colonial careerist who moved out to the Dutch East Indies around 1870, later also working in assorted roles on the coast of China and in Sumatra’s then-developing plantation economy. He was also an active writer, relaying colonial news and market intelligence as well as accounts of his personal experiences in various Finnish newspapers. This article examines the global, cross-border structures that facilitated Björling’s migration to Southeast Asia and his integration there, arguing that his colonial career sketched out the contours of a pre-existing transnational and specifically Nordic subcircuit of Western imperialism that straddled the boundaries of the Dutch and British empires. Moreover, through his correspondences and ventures, Björling sought to establish himself as an active node in that network connecting northern Europe and maritime Asia. The article argues that a careful consideration of ‘outsider’ experiences like Björling’s challenges conventional, nation-centric understandings of nineteenth-century European empires and helps to uncover the multi-layered systems of mobilities that underpinned their expansion around the world.