Road traffic crashes represent a major global public health concern, resulting in millions of fatalities and injuries annually. The burden is particularly severe in low- and middle-income countries, with India experiencing a notable rise in traffic accidents—underscoring the urgent need for improved safety measures and emergency response systems. While recent advances in computer vision and deep learning have enabled real-time accident detection, the prediction of crash severity remains largely unexplored. This article introduces a novel task of crash severity prediction aimed at enhancing emergency response and optimizing resource allocation. We presentCollision Chronicles, a curated dataset of 1500 annotated dashcam videos specifically designed for severity classification. Our proposed architecture combines a TimeSformer backbone for spatiotemporal feature extraction, a bidirectional long short-term memory (LSTM) for sequential modeling, and multihead attention (MHA) for contextual refinement. We also introduce a custom accuracy metric that rewards contextually appropriate predictions, reflecting the ordinal nature of crash severity. Furthermore, we extend our framework to binary accident detection using a balanced dataset of accident and nonaccident videos. To support edge deployment, we apply posttraining dynamic quantization, reducing model size by 3.6$\times$without compromising predictive accuracy. These contributions establish a robust foundation for real-time crash analysis, scalable deployment, and proactive road safety interventions.

Mobility introduces significant challenges for optimal computation offloading, latency minimization, and efficient re source utilization in multi-access edge computing (MEC) systems. A key difficulty lies in leveraging real user trajectories to jointly optimize horizontal (inter-edge) and vertical (edge-to-cloud) task offloading decisions. This paper proposes a two-dimensional offloading scheme for a multi-layer edge–cloud architecture that enables collaborative task execution among resource-constrained edge nodes under mobility conditions. We present MGCO (Mobility-Aware Generative Computation Offloading), a generative AI–driven Transformer-based sequence-to-sequence Deep Q-Network (s2s-DQN) framework that learns from real-time trajectory data to anticipate user movement and optimize task placement dynamically. The Transformer architecture is adopted because its multi-head self-attention effectively captures long range dependencies in mobility and task-demand patterns while avoiding vanishing gradients and sequential bottlenecks inherent to LSTM/GRU models. This design enables parallel contextual reasoning and stable autoregressive action generation, supporting real-time offloading decisions within strict operational latency constraints. Experimental results demonstrate that MGCO consistently outperforms existing methods, achieving up to 41.61% reduction in turnaround time compared to GASTO, and substantial improvements over DMQTO and HMAOA, reaching up to 645.40% and 751.90%, respectively, for longer prediction horizons (48 time slots of 5 seconds each). These results highlight MGCO's robustness, scalability, and effectiveness in managing complex mobility scenarios in dynamic edge–cloud environments.

ABSTRACT The emergence of the Metaverse has introduced significant challenges in task offloading and data processing due to its virtual universe nature with immersive environments and a multitude of interconnected users and devices. The abundance of data in the Metaverse poses security challenges in local processing, necessitating traditional methods such as data transfer to Mobile Edge Computing (MEC) and subsequently to the cloud, thereby emphasizing security concerns. In this paper, a novel approach to address these challenges has been introduced: An Ethereum Blockchain‐based MEC framework uses smart contracts designed to ensure secure task offloading. It enables authentication in the Metaverse through smart contracts, followed by modeling the task offloading issue as a Markov Decision Process (MDP). To solve this MDP problem, a hybrid algorithm integrating Deep Q‐Networks (DQN) with Bidirectional Long Short‐Term Memory (Bi‐LSTM), known as BRL‐Net (Bi‐LSTM Reinforcement Learning Network), has been proposed. This framework enables secure and efficient task offloading in dynamic Metaverse environments. BRL‐Net outperforms Proximal Policy Optimization (PPO), achieving a 9.93% higher reward and greater stability. The BRL‐Net's performance across Blockchain consensus mechanisms shows Delegated Proof of Stake (DPoS) as the most efficient, reducing latency by 49.96%, increasing throughput by 10.48%, and lowering energy consumption by 50.24%, compared to Proof of Stake (PoS), thereby optimizing Metaverse performance.

Global climate change driven by carbon dioxide (CO2) emissions necessitates accurate forecasting for effective mitigation strategies. Traditional forecasting models, such as ARIMA and statistical methods, often struggle to handle the non-linear and long-term temporal dependencies in CO2 emission data. This paper evaluates the performance of advanced Recurrent Neural Network (RNN) architectures, specifically Long Short-Term Memory (LSTM), Stacked LSTM, Bidirectional LSTM (Bi-LSTM), and Gated Recurrent Unit (GRU) with attention mechanisms, in forecasting long-term CO2 emissions. These models are compared against traditional machine learning algorithms like Random Forest and Gradient Boosted Trees (GBT). The Random Forest and Gradient Boosted Trees, implemented using PySpark framework for distributed computing, are leveraged for efficient big data handling, enabling scalable solutions for CO2 emission forecasting. Our experimental results, using metrics such as Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE), show that the Stacked LSTM model achieved the lowest MAE of 25.795, RMSE of 80.678, and MAPE of 1.072, outperforming Random Forest (MAE: 72.4, RMSE: 310.5, MAPE: 1.32) and GBT (MAE: 75.6, RMSE: 356.8, MAPE: 1.57). The findings demonstrate that advanced RNN models, particularly the Stacked LSTM and Bi-LSTM with attention, provide superior performance over traditional models, effectively capturing complex temporal patterns in the data. This study offers valuable insights into CO2 emission forecasting approaches, contributing to the development of accurate tools for climate policy and strategy formulation.

Climate change and global warming demand immediate global action to protect future generations. Although deep learning is crucial for solving complex tasks, its high energy consumption and carbon emissions from extensive training processes necessitate improvements in efficiency to reduce its environmental impact. This research addresses the need for accurate carbon emission tracking to mitigate climate change and promote sustainable development. We evaluated deep learning models—specifically, the Temporal Fusion Transformer (TFT), Attention-based LSTM (Att-LSTM), and standard LSTM—in predicting CO2 emissions using datasets from World Bank Indicators (WBI) and Our World in Data (OWID). The results highlight TFT’s superiority, achieving the lowest mean squared error (MSE) in validation (0.0029) compared to Att-LSTM (0.0070) and LSTM (0.0072). Additionally, TFT demonstrates a lower root mean squared error (RMSE) in validation (0.0539) than both Att-LSTM (0.0712) and LSTM (0.0909), showcasing its enhanced predictive accuracy across evaluation tools like CarbonTracker, eco2AI, and CodeCarbon. Furthermore, TFT consistently outperforms LSTM in computational efficiency and carbon emissions, despite LSTM’s lower power usage but higher emissions in specific scenarios. These findings underscore TFT's pivotal role in formulating effective climate policies and promoting renewable energy sources, emphasizing its potential to significantly contribute to global warming mitigation and sustainability.

Rapid technical advancements in recent times have made it possible to produce a class of affordable unmanned aerial vehicles (UAVs), which can serve a wide range of public and private users for different industrial or non-industrial requirements. It is very complex to set up a communication network among the UAVs, which is very essential for completing any task like collecting data in extreme weather conditions. For UAV placement, we have suggested a distributed approach using PPO-based reinforcement learning; here, UAVs are utilized as aerial access points to create a mesh network that connects to ground nodes positioned in a designated area and can collect data from ground nodes to predict weather conditions. Our objective is to fulfill each ground node’s data rate requirements while deploying the fewest number of UAVs possible to cover it. The convex hull formation of swarm uavs satisfy our objective, which arranges the n uavs on a convex hull’s vertex. In this study, we present a deep reinforcement learning-based method for building swarm uavs with convex hull patterns in the euclidean plane. The existing research on convex hull pattern generation uses heuristic and combinatorial optimization techniques and evaluates performance in terms of time and space consumed. Convex hull patterns increase the mutual visibility of swarm UAVs, which helps in the cooperation of UAVs. In our research for the first time, convex hull pattern formation around a given target point is accomplished using PPO-based Deep Reinforcement Learning (DRL).

Accurate forecasting of critical pollutants such as NO2and PM2.5 is essential in the ongoing battle against air pollution. This paper introduces ConvTrans, a novel hybrid architecture that integrates convolutional and Transformer technologies to enhance the precision of PM2.5 and NO2predictions. Our evaluations demonstrate that ConvTrans improves PM2.5 prediction accuracy, increasing the R-squared value by 3.6%, reducing RMSE by 5.4%, MSE by 7.5%, and MAE by 17.3%. Similarly, for NO2predictions, the model shows a 4.2% increase in R-squared, along with a 5.4% reduction in RMSE, a 13.7% decrease in MSE, and a 5.6% reduction in MAE. These results highlight the model’s effectiveness in short-term forecasting, addressing key challenges in environmental management.

Air pollution presents serious threats to public health and environmental sustainability, especially when it comes to concentrations of ozone (O3). Even though they are accurate, traditional monitoring methods have spatial limitations that cause gaps in the coverage of data. In order to tackle this issue, we provide a hybrid deep learning model that predicts O3levels in different parts of India by integrating Transformer and Convolutional Neural Networks (CNN) using satellite and meteorological data. While the Transformer model uses meteorological data to identify temporal trends, the CNN component analyzes satellite pictures to extract spatial information. Multiple assessment criteria, including R2, MSE, RMSE, and MAE, show that we enhance prediction accuracy by merging both models. Our model performs better in both short- and long-term predictions than other deep learning techniques and RNN, such as CNN, GRU, and LSTM models. The results suggest that the hybrid CNN-Transformer model is a promising solution for accurate air pollution forecasting, offering critical insights for timely interventions and policy development.

Air quality is a critical factor influencing public health, with pollutants such as particulate matter (PM10, PM2.5), ground-level ozone (O3), carbon monoxide (CO), sulfur dioxide (SO2), and nitrogen dioxide (NO2) posing significant health risks. Understanding the relationship between these pollutants is essential for effective air quality management. In this study, we apply a comprehensive range of statistical models, including Multivariate Analysis of Variance (MANOVA), Canonical Correlation Analysis (CCA), Factor Analysis, post-hoc tests, linear regression, and other techniques to evaluate the correlation between PM2.5 and key air pollutants. By employing these methods, we aim to identify the most significant contributors to PM2.5 levels and provide insights to inform air quality control strategies. Our findings offer a robust statistical framework for predicting PM2.5 concentrations, enhancing the ability of policymakers and environmental agencies to mitigate pollution-related health risks.

The rapid growth of mobile data and computing demands has strained resource-constrained edge devices, particularly in supporting IoT applications. Mobile Edge Computing (MEC) offloading alleviates these challenges by shifting complex tasks to edge-cloud servers, reducing computational burdens and enhancing efficiency. The integration of 5G and 6G technologies further enhances MEC by providing ultra-low latency and high-bandwidth connections. Despite the use of deep learning methods in task offloading, current approaches struggle with slow learning and adaptability issues. To address these challenges, we introduce a Deep Meta Reinforcement Learning based Offloading (Deep Meta-RL) Framework. Formulating the task offloading problem as a Markov Decision Process (MDP) enables us to leverage the Deep Meta-RL algorithm for precise offloading decisions, reinforcement learning’s decision-making, and meta-learning’s adaptability. Simulation results demonstrate that Deep Meta-RL significantly outperforms traditional DQN algorithms, achieving a 16.75% improvement in rewards and reducing latency by 20%.

This research work introduces a new strategy to enhance computation offloading in wireless-powered Edge-Enabled Mobile Computing (EEMC) networks by utilizing Dueling Double Deep Q-Networks with Attention (Dueling DDQN-A). EEMC facilitates the offloading of computational tasks from mobile devices to local edge servers, leading to decreased latency and improved energy efficiency. However, the dynamic and stochastic nature of wireless environments presents significant challenges for real-time task offloading decisions. To address this, we enhance the Dueling DDQN framework by integrating an attention mechanism to prioritize the most relevant features in the state space. The proposed Dueling DDQN-A model enhances decision-making by separating state value estimation from action advantage estimation. Additionally, the model uses attention to prioritize key state features, leading to improved adaptability in dynamic wireless channel and network environments. We frame the offloading decision problem as a Markov Decision Process (MDP) and apply deep reinforcement learning to optimize both the computation rate and energy efficiency. We compare and evaluate the model through comprehensive simulations using the latest techniques like Simple DQN, Double DQN, and Dueling DDQN. Our results demonstrate that Dueling DDQN-A achieves a 59.12% improvement in average computation rate and a 16.19% reduction in energy consumption over the baseline models. Additionally, it significantly outperforms the baselines in terms of training loss and convergence speed. These findings suggest that Dueling DDQN-A offers a robust and highly efficient solution for real-time computation offloading in wireless-powered EEMC networks, making it a strong candidate for deployment in real-world scenarios.

Health assessment (HA) & remaining useful life (RUL) estimation, the two essential pillars of the prognostics and health management (PHM) paradigm, help improve industrial equipment reliability while reducing maintenance costs. However, reported works treat HA and RUL estimation as disjoint problems though there exist unexploited similarities among these related issues. Additionally, in practical industrial working scenarios, equipment(s) stay in a normal condition for most of its lifespan, leading to a disproportionate training dataset, hampering the prediction accuracy. To overcome the above problems, we propose a data-driven multi-task learning framework aided by a novel least squares recurrent auxiliary classifier generative adversarial network (LS-R-ACGAN). LS-R-ACGAN employs recurrent neural networks (RNNs) in its generator & discriminator networks for multi-variate fault data generation while overcoming the vanishing gradient problem of ACGANs. Post-data-augmentation, a balanced training dataset, trains a multi-task learning model based on a deep gated RNN (DGRU) for joint HA and RUL estimation. Our simulations use the C-MAPSS dataset for testing the proposed approach’s accuracy. The final results showcase improvements by at least$\sim$3.54% and$\sim$2.38% on the RMSE and Score metric over existing state-of-the-art works suggesting its competitiveness and competence for real-world implementations.Note to Practitioners—This study attacks multiple presumptions concerning the maintenance of complex systems including, ample availability of fault samples alongside normal samples and solving only one of the problems, i.e., health assessment (HA) or remaining useful life (RUL) estimations rather than both. Popular artificial intelligence (AI) based solutions rely heavily upon the quantity of training data for desired performance outcomes. However, in real-life industrial scenarios, fault instances are rare, inherently resulting in an imbalanced dataset. Additionally, it is both labor-intensive and costly to repeat multiple experiments for gathering required data as the equipment(s) pass through short-lived fault states. Thus conventional AI-based solutions may end up producing biased predictions. This work proposes to solve the imbalanced training dataset problem along with joint HA and RUL estimations, in one solution pipeline, currently unexplored in majority of the existing works in the literature. LS-R-ACGAN generates ample diverse fault samples for reducing the degree of imbalance between normal and fault classes. Subsequently, after data augmentation, the balanced training dataset helps train a multi-task learning model for joint HA and RUL estimations. The proposed framework has been implemented and tested on a benchmark dataset proving its superiority over multiple existing methods in the literature.

Resource-constrained edge devices can not efficiently handle the explosive growth of mobile data and the increasing computational demand of modern-day user applications. Task offloading allows the migration of complex tasks from user devices to the remote edge-cloud servers thereby reducing their computational burden and energy consumption while also improving the efficiency of task processing. However, obtaining the optimal offloading strategy in a multi-task offloading decision-making process is an NP-hard problem. Existing Deep learning techniques with slow learning rates and weak adaptability are not suitable for dynamic multi-user scenarios. In this article, we propose a novel deep meta-reinforcement learning-based approach to the multi-task offloading problem using a combination of first-order meta-learning and deep Q-learning methods. We establish the meta-generalization bounds for the proposed algorithm and demonstrate that it can reduce the time and energy consumption of IoT applications by up to 15%. Through rigorous simulations, we show that our method achieves near-optimal offloading solutions while also being able to adapt to dynamic edge-cloud environments.

UAVs have the potential to enhance wireless systems by improving range and quality, and this can be achieved by utilising the Mobile Edge Computing provided by the unmanned aerial vehicle. In this system, the MEC server mounted on the unmanned aerial vehicle can offer offload services to all the User Equipment in a given space. By offloading some proportion of its tasks to unmanned aerial vehicle for computation, the UE can perform the remaining tasks locally. The objective of this study is to minimize the maximum processing delay in the whole process by optimizing on four parameters, scheduling of the user equipment, portion of the task that has to be offloaded, angle of flight for the UAV, and speed of flight of the UAV, taking into account discrete variables and power constraints. However, due to the nonconvexity, the state space’s high dimension, and action’s space continuous nature of this problem, we are proposing a Proximal Policy optimization algorithm which is based on boosting the policy gradient. Further we will do a comparative analysis of PPO algorithm with other popular Reinforcement Learning algorithms, particularly the Deep Deterministic Policy Gradient algorithm. PPO algorithm can quickly achieve the optimal policy for offloading computation in a dynamic environment. The results obtained implies that the both PPO and DDPG algorithm converges quickly, and the processing delay is minimizd. However, our proposed PPO algorithm has shown significant improvement in minimizing the processing delay. Our model also performs way better compared to basic algorithm such as Deep Q Network (DQN).

Datacenter carbon emissions are rising, which poses a serious issue that must be addressed quickly. We may see differences in emissions when we look at the carbon intensity of the electrical system since various areas have different energy sources. We can time the execution of workloads to occur during periods when the carbon intensity is lower by using this temporal variability. This paper aims to address the challenge of reducing carbon emissions in cloud computing by proposing a framework for workload shifting based on low carbon intensity periods in the power grid. The study focuses on four countries and their carbon production in the year 2022, calculating the carbon intensity for each country. Additionally, the paper identifies different cloud computing workloads and integrates constraints such as power, SLA, carbon emissions, and routing into the framework. The crucial factors considered during workload shifting include geo-distributed load balancing and right-sizing the data center.A simulation is developed to evaluate the proposed framework, simulating scenarios with shiftable workloads. The results are compared and analyzed, assessing the framework’s effectiveness in reducing carbon emissions while meeting the specified constraints. The findings highlight the potential benefits of workload shifting in reducing carbon emissions and improving environmental sustainability. Overall, this research contributes to the advancement of green computing and offers insights into the development of sustainable cloud computing practices.

Climate change and global warming are urgent environmental issues demanding immediate action to safeguard future generations. The major contributor to the greenhouse effect, carbon dioxide $\left(\mathrm{CO}_{2}\right)$, primarily originates from industrial and transportation fossil fuel combustion. International agreements, like the Paris Agreement, call for a 30-35% reduction in CO2emissions compared to 2005 levels. This research aims to predict CO2emissions and raise awareness among SAARC nations and governments about the increasing trend. We introduce a novel predictive framework using Attention-based Long Short-Term Memory (A-LSTM) for CO2emissions analysis. The Attention mechanism assigns variable weights to input data, facilitating indirect connections between LSTM outputs and pertinent inputs. This enhances resource allocation in the A-LSTM model, overcoming computational constraints. We integrate input parameters encompassing CO2emissions from land-use changes, oil, natural gas, and coal combustion to forecast CO2emissions and correlate them with population and per capita GDP. Our comparative analysis conclusively demonstrates the superior performance of A-LSTM models over baseline LSTM models when applied to the CO2emission dataset sourced from Our World in Data (OWID) and World Bank Indicator database. Specifically, the LSTM model registers a MAPE of 24.968 and an RMSE of 0.34, whereas the Attention-based LSTM model showcases a marked improvement of 57% with a considerably lower MAPE of 10.5902 and an RMSE of 0.107.

The design and development of energy and spectrum-efficient solutions are important in the success of the Internet of Things (IoT). Due to the presence of an enormous number of smart devices, such as sensors, actuators, and different household devices achieving such scalable and efficient solutions are challenging. A wireless sensor network (WSN) with dynamic spectrum access (DSA) capability, known as the cognitive radio sensor network (CRSN) is recently introduced to deal with spectrum scarcity problem. Although the spectrum scarcity is reduced with DSA paradigm, the energy-efficient solutions are still required to be addressed due to the involvement of energy constrained devices in CSRN. Clustering is one of the efficient ways to optimize the energy consumption in the networks. Due to combination of both WSN and cognitive radio network (CRN), existing solutions of WSN and of CRNs are not applicable to CRSN. In this article, we propose a neighbor discovery algorithm and two greedy k-hop clustering schemes (k-SACB-WEC and k-SACB-EC) for CRSN with the aim focusing on IoT applications, which require constant intracluster and intercluster communications. We focus on achieving bichannel connectivity while maximizing network life. In our clustering different parameters, such as nodes' residual energy, spectrum awareness, appearance probability of primary users (PUs) of channels, channel quality, robustness on PUs' arrival, and the Euclidean distance between nodes are taken into consideration to select the hop count and common channels for clusters. Through simulation, we have highlighted the performance improvements of our proposed schemes in terms of the lifetime of the network, number of clusters, stability of networks, and frequency of reclustering over recently reported clustering algorithm in CRSN. The simulation results show that k-SACB-WEC generates at least 40% less number of clusters as compared to k-SACB-EC, network stability-aware clustering (NSAC), Prolong-SEP (PSEP), SAC-WCM, and Cognitive LEACH (CogLEACH). Also, in terms of network stability, the k-SACB-WEC achieves at least approximately 100% higher number of rounds before the first node dead than the compared competitive approaches.

In this article, we first formulate the joint resource allocation, interference minimization, user-level, and cell-level fairness for maximum resource reuse in 5G heterogeneous small cell networks as an NP-hard problem. We then propose three algorithms - centralized, distributed, and randomized distributed algorithms - to efficiently solve the formulated resource allocation problem while minimizing interference, maximizing fairness, and resource reuse. Through extensive real data analysis and network simulations, we show that our proposed solutions outperform state-of-the-art schemes, namely interfering model (INT) and distributed random access (DRA), for both low and high-density 5G networks.

The large number of internet-connected devices will continue to drive growth in data traffic in an exponential way, forcing network operators to increase the capacity of wireless networks. To do so in the cost-effective way a paradigm shift is occurring in 5G cellular networks from high power macro base station to small cell heterogeneous networks known as microcells, picocells, and femtocells. This paradigm shift of 5G cellular networks gives many opportunities ranging from increase capacity to reuse the scarce spectrum resources to coexistence to interference minimization etc. The coexistence of heterogeneous small cells makes the resource allocation, interference management, and maximum fairness among the users more complicated. In this paper, we formulate the resource allocation for spectrum reuse maximization, interference minimization and user level fairness in heterogeneous small cells 5G cellular networks as a NP-hard problem. We design centralized and probability based heuristic for the above resource allocation problem in-order to minimize interference and to achieve maximum spectrum reuse and fairness among the users in feasible computational complexity. We show through extensive network simulations that our proposal outperforms existing centralized interfering model (INT) and distributed random access (DRA) in both low and high-density networks.

Next-generation wireless networks are going to have highly dense, small cell structure with a large number of femtocells. The dense deployment of the femtocell network architecture is expected to meet the growing data demand by leveraging millimeter-wave structure of 5G wireless networks. However, arbitrary deployment of large number of femtocells underlying a macrocell will pose a challenge for collision and confusion-free Physical Cell ID (PCID) assignments as the total number of available PCIDs is limited to 504. In this paper we propose a distributed, randomized k-clustering algorithm for collision and confusion-free PCID assignment problem, which is known to be NP-complete. To reduce the total control message flow, we create overlapping clusters in ultra-dense femtocellular networks, where each cluster head runs the distributed randomized PCID allocation algorithm and locally monitors the conflicts to avoid the collision and confusion constraints. We prove the correctness of our proposed algorithm and analyze its time and message complexity. Through simulation experiments, we also show the effect of different parameters on the PCID allocation objectives.

Road congestion has become a major problem in cities in developing countries resulting in massive delays, wastage of fuel and road accidents. For proper handling it is essential to observe the road congestion patterns. Methods like on-road cameras, etc. require huge investments whereas crowdsourcing methods generate large amount of redundant data. This paper presents a new approach using event sensing to capture relevant crowdsourced data to estimate road traffic congestion and utilizes MapReduce for generating analytics efficiently.

In this poster, we present a new lightweight multithreaded software model for target location detection system. Our localization algorithm is based on trilateration using RSSI for ranging.This work can be applied for collecting the location information of mobile sensor nodes in the network. Our software runs on the sensor nodes and on the base station. To support our clam we performed experimental analysis on telosb motes.

Smartphone sensing for large data set collection of potholes is useful for monitoring transportation system intelligently. Collecting large data set of different information sources such as frequency of taking brakes, honks, potholes,black-spots etc becomes gigantic task by manual method. We have developed smartphone sensing based method which uses crowdsourcing to collect real large data set of pothole data from the field. In this work the method to locate and sense potholes using sensors in smartphones is discussed along with its implimentation results.We have shown how we have used crowdsourcing techniques to collect large data set on potholes efficiently using sensors available in smartphones and processing the data obtained as required for our purpose.

Vehicles equipped with communication capability, allows for inter-vehicle communication. Vehicular ad hoc network(VANET) is a subclass of MANET having no fixed infrastructure and rely on nodes to provide network functionality. Vehicular ad hoc network is dominated by its unique characteristics of rapid but predictable topology changes with frequent fragmentation and small effective network diameter. Such networks on road often forms intermittent connections. The draw back of store-and carry forward is that it can serve unidirectional delay-tolerant communication. In this paper we have introduced a compass labeling scheme for broadcast communication in VANETs exploiting opportunistic intermittent bi-directional communication.

Location tracking of mobile entity becomes a research challenge for applications in vehicular networks, mobile sensor networks and mobile adhoc networks. The GPS based tracking in vehicular networks has limitations in certain scenarios such as inside tunnels, tall buildings, dense forests, etc. Tracking of fast-paced mobile entity (say mobile node) finds applications in vehicular networks such as chasing, hit-and-run accidents, etc. Connected dominating set(CDS) provides a virtual backbone in adhoc network. This paper uses connected dominating set(CDS) based virtual backbone in vehicular adhoc networks for tracking of fast-paced mobile node's location. We show that using a simple heuristics that the location updates for mobile node can be reduced to the tune of 40%.

A minimum connected dominating set (MCDS) is used as virtual backbone for efficient routing and broadcasting in ad hoc sensor networks. The minimum CDS problem is NP-complete even in unit disk graphs. Many heuristics-based distributed approximation algorithms for MCDS problems are reported and the best known performance ratio has (4.8 + 1n 5). We propose a new heuristic called collaborative cover using two principles: 1) domatic number of a connected graph is at least two and 2) optimal substructure defined as subset of independent dominator preferably with a common connector. We obtain a partial Steiner tree during the construction of the independent set (dominators). A final postprocessing step identifies the Steiner nodes in the formation of Steiner tree for the independent set of G. We show that our collaborative cover heuristics are better than degree-based heuristics in identifying independent set and Steiner tree. While our distributed approximation CDS algorithm achieves the performance ratio of (4.8 + 1n 5) opt + 1.2, where opt is the size of any optimal CDS, we also show that the collaborative cover heuristic is able to give a marginally better bound when the distribution of sensor nodes is uniform permitting identification of the optimal substructures. We show that the message complexity of our algorithm is O(n¿2), ¿ being the maximum degree of a node in graph and the time complexity is O(n).

The mobility of nodes with a purpose called as robotic-mobility is used for replenishing energy to strategic military applications. Connected dominating set(CDS) provides a virtual backbone in adhoc network. Assuming a CDS based backbone in place, we give a technique of maintaining the location of mobile node for a given topology setting. We show that using a weighted CDS reduces the maintenance of location updates for mobile node. The complexity of our mobile-node maintanence algorithm is at most O(d. log d) where d is number of boundary crossings in the single node's movement. The simulation shows that our weighted CDS method for mobile-nodes achieve 40\% reduction in location maintenance using to shortest-hop path in CDS.

Wireless ad hoc and sensor networks (WSNs) often require a connected dominating set (CDS) as the underlying virtual backbone for efficient routing. Nodes in a CDS have extra computation and communication load for their role as dominator, subjecting them to an early exhaustion of their battery. A simple mechanism to address this problem is to switch from one CDS to another fresh CDS, rotating the active CDS through a disjoint set of CDSs. This gives rise to the connected domatic partition (CDP) problem, which essentially involves partitioning the nodes V(G) of a graph G into node disjoint CDSs. We have developed a distributed algorithm for constructing the CDP using our maximal independent set (MlS)-based proximity heuristics, which depends only on connectivity information and does not rely on geographic or geometric information. We show that the size of a CDP that is identified by our algorithm is at least [delta+1/beta(c+1)] - f, where delta is the minimum node degree of G, beta les 2, c les 11 is a constant for a unit disk graph (UDG), and the expected value of f is epsidelta|V|, where epsi Lt 1 is a positive constant, and delta ges 48. Results of varied testing of our algorithm are positive even for a network of a large number of sensor nodes. Our scheme also performs better than other related techniques such as the ID-based scheme.

Abstract Nodes in wireless sensor networks (WSN) are deployed in an unattended environment with non re‐chargeable batteries. Thus, energy efficiency becomes a major design goals in WSNs. Clustering becomes an effective technique for optimization energy in various applications like data gathering. Although aggregation aware clustering addresses lifetime and scalability goals, but suffers from excessive energy overhead at clusterhead nodes. Load balancing in existing clustering schemes often use rotation of clusterhead roles among all nodes in order to prevent any single node from complete energy exhaustion. We considered important aspects of energy and time overhead in rotation of the clusterhead roles in various node clustering algorithms with goals to further prolong the network lifetime by minimizing the energy overheads in rotation setup. The problem of clusterhead rotation is abstracted as the graph‐theoretic problem of domatic partitioning, which is also NP‐complete. The dense deployment and unattended nature rules out the possibility of manual or external control in existing domatic partition (DP) techniques to be used for WSNs. To our knowledge, no self‐organizing technique exists for domatic partitioning. We developed a distributed self‐organizing one‐domatic partitioning scheme with approximation factor of at least 1/16 for unit‐disk‐graphs (UDGs). In this work, we demonstrate that the benefits of self‐organization is achieved without sacrificing the quality of domatic partitioning. We demonstrated through simulations that our self‐organizing DP without sacrificing on the size of DP achieves self‐organization capability which is able to reduce time and energy overheads of clusterhead rotation resulting to an improved network lifetime compared to the existing clustering protocols for sensor networks. Copyright © 2008 John Wiley & Sons, Ltd.