While Federated learning (FL) is considered privacy-preserving by nature, it remains vulnerable to many attacks, such as data and model poisoning, that compromise data integrity and model accuracy. Conventional privacy-preserving federated learning (PPFL) mechanisms, including homomorphic encryption (HE), secure aggregation, and secure multiparty computation (SMPC) demonstrate several limitations, such as high computational complexity, significant communication overhead, and scalability challenges. To overcome the aforementioned issues, we propose an end-to-end secure FL architecture that integrates differential privacy (DP), zero-knowledge proof (ZKP), and median aggregation. DP prevents data leakage during model updates by introducing Laplacian noise for privacy preservation. ZKP is implemented through Schnorr’s protocol, which enables lightweight and efficient client authentication without revealing sensitive information. Finally, median aggregation is incorporated to mitigate the impact of outliers and adversarial updates, ensuring robust prediction aggregation. The experimental results indicate that the proposed approach outperforms other well-known PPFL methods including partially homomorphic encryption (PHE), fully homomorphic encryption (FHE) and SMPC. It delivers substantial improvements in global accuracy, especially for larger client counts, with gains of 10%-30% over the other methods. The client training time is significantly reduced by 70%-90%, ensuring faster processing. The approach also excels at reducing average round latency by 80%-95%, enhancing the overall efficiency of the system. Communication overhead is significantly reduced by 65%-85%, lowering data transfer costs per round. Furthermore, the size of the model is minimized by 60%-85%, making it more resource efficient and scalable for larger deployments.

The Internet of Things (IoT) has garnered significant attention from both industries and the research community. The diverse nature of IoT devices makes them a prime target for cybercriminals. An intelligent intrusion detection system (IDS) can quickly identify multiple types of cyberattacks within an IoT system. However, operational efficiency and safety become challenging issues when managing limited labeled data in IoT networks. This article proposes a novel cyberattack detection scheme using few-shot learning (FSL). The proposed scheme employs a self-attention mechanism with a Siamese network that learns to recognize normal and malicious network traffic patterns through FSL. The Siamese network architecture facilitates efficient sample comparison by learning a shared representation. Incorporating an attention mechanism further enhances its ability to focus on discriminative features, improving attack detection accuracy for rapidly evolving intrusion patterns. The effectiveness of the proposed framework is evaluated through extensive experiments on the latest Edge-IIoTset dataset. The experimental findings demonstrate that the proposed IDS achieved the best accuracy of 99.75% with the complete dataset. In a few-shot performance evaluation, the designed architecture achieved the best accuracy of 78.69%, 81.19%, and 81.83% for 1 shot, 5 shots, and 10 shots, respectively.

Blockchain is expected to play a key role in securing next generation communication systems, i.e., B5G and 6G, which will be highly decentralised, with high integration of edge computing, device-to-device (D2D) communications, and notably IoT networks. This paper addresses a fundamental bottleneck of blockchain; the simulation of consensus algorithms. State-of-the-art blockchain consensus algorithm simulators are built on general data that do not consider resource-constrained devices. These simulators have limitations in performance measurement (energy, latency, and throughput) and testing of security attacks, including DoS, Sybil, and 34% or 51% attacks). This paper introduces a blockchain Internet of Things consensus algorithm (BICA) simulator, which offers a framework for testing consensus algorithms with adaptable IoT data in various attack scenarios. It evaluates metrics such as latency, throughput, and attack resilience, providing insights into their capabilities under diverse network conditions. A case study involving Proof of Stake (PoS), Delegated Proof of Stake (DPoS), Proof of Elapse Time (PoET), Proof of Authority (PoA) and Practical Byzantine Fault Tolerance (PBFT) showed PBFT’s superior performance and security against vulnerabilities such as Sybil, DoS, and 34-51% attacks. BICA’s block-creation speed surpasses that of the existing simulators.

This paper proposes an integrated approach called FL-RSD, leveraging the key advantages of Federated Learning (FL) and Recursive Self-Distillation (RSD) for malware detection in the Internet of Vehicles (IoV). The proposed FL-RSD framework enhances model generalization, mitigates overfitting to non-IID data, and improves adaptability to new malware variants. The RSD process iteratively transfers knowledge from a teacher model to a lightweight student model, reducing model complexity and communication overhead while preserving detection accuracy. Experimental results have confirmed that FL-RSD achieves significant performance improvements over baseline models in terms of malware detection accuracy and adversarial robustness. FL-RSD attains a malware detection accuracy and an average adversarial robustness scores over 92%, outperforming Federated Proximal, FedNova, and Hierarchical FL. The improvements range from 3% to 48% for detection accuracy, and 6% to 81% for the adversarial robustness score. Additionally, FL-RSD demonstrates a minimal memory footprint with a final global model size of 373.17 KB and maintains knowledge retention with an Average Catastrophic Forgetting Score of 93.66%. These results confirm that FL-RSD offers a lightweight, efficient, and scalable solution for malware detection in IoV.

Many Internet of Things (IoT) applications can benefit from Social Web of Things (S-WoT) methods that enable knowledge discovery and help solving interoperability problems. The semantic modeling of S-WoT is the main emphasis of this work where we suggest a novel solution, evolutionary clustering for ontology matching (ECOM), to explore correlations between S-WoT data using clustering and evolutionary computation methodologies. The ECOM approach uses a variety of clustering techniques to aggregate S-WoT data's strongly related ontologies into comparable categories. The principle is to match concepts of similar groups rather than full concepts of two ontologies, which necessitates splitting examples of each ontology into similar groups. We design two clustering algorithms for ontology matching using conventional methods, as well as sophisticated clustering techniques. Moreover, we develop an intelligent matching algorithm that uses evolutionary computation to quickly converge to (or ideally identify) optimal matches. Numerous simulations have been conducted using various ontology databases to demonstrate the application and precision of ECOM. Our findings clearly show that ECOM has better results when compared to cutting-edge ontology matching methods. The F-measure of ECOM exceeds 95% whereas it does not reach 90% for all baseline methods. The results also confirm that ECOM scales with big data in S-WoT environments.

This study introduces an innovative method aimed at reducing energy consumption in sensor networks by predicting sensor data, thereby extending the network’s operational lifespan. Our model, Temporal Graph Sensor Prediction (TG-SPRED), predicts readings for a subset of sensors designated to enter sleep mode in each time slot, based on a non-scheduling-dependent approach. This flexibility allows for extended sensor inactivity periods without compromising data accuracy. TG-SPRED addresses the complexities of event-based sensing—a domain that has been somewhat overlooked in existing literature—by recognizing and leveraging the inherent temporal and spatial correlations among events. It combines the strengths of Gated Recurrent Units and Graph Convolutional Networks to analyze temporal data and spatial relationships within the sensor network graph, where connections are defined by sensor proximities. An adversarial training mechanism, featuring a critic network employing the Wasserstein distance for performance measurement, further refines the predictive accuracy. Comparative analysis against six leading solutions using four critical metrics—F-score, energy consumption, network lifetime, and computational efficiency—showcases our approach’s superior performance in both accuracy and energy efficiency.

A novel distributed particle filter algorithm for target tracking is proposed in this paper. It uses new metrics and addresses the measurement uncertainty problem by adapting the particle filter to environmental changes and estimating the kinematic (motion-related) parameters of the target. The aim is to calculate the distance between the Gaussian-distributed probability densities of kinematic data and to generate the optimal distribution that maximizes the precision. The proposed data fusion method can be used in several smart environments and Internet of Things (IoT) applications that call for target tracking, such as smart building applications, security surveillance, smart healthcare, and intelligent transportation, to mention a few. The diverse estimation techniques were compared with the state-of-the-art solutions by measuring the estimation root mean square error in different settings under different conditions, including high-noise environments. The simulation results show that the proposed algorithm is scalable and outperforms the standard particle filter, the improved particle filter based on KLD, and the consensus-based particle filter algorithm.

This work targets a comprehensive model enabling energy-constrained IoT (Internet of Things) sensor devices to be inactive for extended periods while estimating their readings of real-time events. Although events seem semantically uncoupled, they are usually spatially and temporally related. We propose GSAVES (Graph Sensor AdVersarial for Energy Saving), which uses readings from active devices and spatial correlations to generate the missing data due to sensor inactivity. The missing readings are generated with Graph Convolutional Network (GCN) that learns embeddings from data and the graph structure. GSAVES is evaluated against four state-of-the-art solutions using three network sizes and four performance metrics. The results demonstrate the efficiency of GSAVES for providing the best balance between the considered metrics, outperforming all the solutions in reducing energy consumption and improving accuracy.

This research presents a new generic deep learning (DL) framework for anomaly detection in the Internet of Everything (IoE). It combines decomposition methods, deep neural networks, and evolutionary computation to better detect outliers in IoE environments. The data set is first decomposed into clusters, while similar observations in the same cluster are grouped. Five clustering algorithms were used for this purpose. The generated clusters are then trained using DL architectures. In this context, we propose a new recurrent neural network for training time-series data. Two evolutionary computational algorithms are also proposed: 1) the genetic and 2) the bee swarm, to fine-tune the training step. These algorithms consider the hyperparameters of the trained models and try to find the optimal values. The proposed solutions have been experimentally evaluated for two use cases: 1) road traffic outlier detection and 2) network intrusion detection. The results show the advantages of the proposed solutions and a clear superiority compared to state-of-the-art approaches.

This paper introduces a novel deep learning architecture for identifying outliers in the context of intelligent transportation systems. The use of a convolutional neural network with decomposition is explored to find abnormal behavior in maritime data. The set of maritime data is first decomposed into similar clusters containing homogeneous data, and then a convolutional neural network is used for each data cluster. Different models are trained (one per cluster), and each model is learned from highly correlated data. Finally, the results of the models are merged using a simple but efficient fusion strategy. To verify the performance of the proposed framework, intensive experiments were conducted on marine data. The results show the superiority of the proposed framework compared to the baseline solutions in terms of several accuracy metrics.

Significant efforts have been made for vehicle-to-vehicle communications that now enable the Internet of Vehicles (IoV). However, current IoV solutions are unable to capture traffic data both accurately and securely. Another drawback of current IoV models that are based on deep learning is that the methods used do not tune hyperparameters efficiently. In this paper, a new system known as Secure and Intelligent System for the Internet of Vehicles (SISIV) is developed. A deep learning architecture based on graph convolutional networks and an attention mechanism are implemented. In addition, blockchain technology is used to protect data transmission between nodes in the IoV system. Moreover, the hyperparameters of the generated deep learning model are intelligently selected using a branch-and-bound technique. To validate SISIV, experiments were conducted on four networked vehicle databases dealing with prediction problems. In terms of forecasting rate ($>$90%), F-measure ($>$80%), and attack detection (< 75%), the results clearly show the superiority of SISIV over baseline systems. Moreover, compared to state-of-the-art solutions based on traffic prediction, SISIV enables efficient and reliable prediction of traffic flow in an IoV context.

This paper explores the vehicle detection problem and introduces an improved regional convolution neural network. The vehicle data (set of images) is first collected, from which the noise (set of outlier images) is removed using the SIFT extractor. The region convolution neural network is then used to detect the vehicles. We propose a new hyper-parameters optimization model based on evolutionary computation that can be used to tune parameters of the deep learning framework. The proposed solution was tested using the well-known boxy vehicle detection data, which contains more than 200,000 vehicle images and 1,990,000 annotated vehicles. The results are very promising and show superiority over many current state-of-the-art solutions in terms of runtime and accuracy performances.

This article introduces a new model to identify a group of trajectory outliers from a large trajectory database and proposes several algorithms. These can be split into three categories: 1) algorithms based on data mining and knowledge discovery, which study the different correlations among the trajectory data and identify the group of abnormal trajectories from the knowledge extracted; 2) algorithms based on machine learning and computational intelligence methods, which use the ensemble learning and metaheuristics to find the group of trajectory outliers; and 3) an algorithm exploring the convolution deep neural network that learns the different features of historical data to determine the group of trajectory outliers. Experiments on different trajectory databases have been carried out to investigate the proposed algorithms. The results show that the deep learning solution outperforms data mining, machine learning, and computational intelligence solutions, as well as state-of-the-art solutions in terms of runtime and accuracy performance.

Road safety is tackled and an intelligent deep learning framework is proposed in this work, which includes outlier detection, vehicle detection, and accident estimation. The road state is first collected, while an intelligent filter, based on SIFT extractor and a Chinese restaurant process is used to remove noise. The extended region-based convolution neural network is then applied to identify the closest vehicles to the given driver. The residual network will benefit from the vehicle detection process to make a binary classification on whether the current road state might cause an accident or not. Finally, we propose a novel optimization model for optimizing hyper-parameters in deep learning methodologies by using evolutionary computation. The proposed solution has been tested using benchmark vehicle detection and accident estimation datasets. The results are very promising and show superiority over many current state-of-the-art solutions in terms of runtime and accuracy, where the proposed solution has more than 5% of improved accident estimation rate compared to the conventional methods.

Prediction of sensor readings in event-based Internet-of-Things (IoT) applications is considered. A new approach is proposed, which allows turning off sensors in periods when their readings can be predicted, thus preserving energy that would be consumed for sensing and communications. The proposed approach uses a long short-term memory (LSTM) model that learns spatiotemporal patterns in sequences of sensorial data for future predictions. The LSTM model and the sensors collaboratively monitor the environment. They are controlled by a reinforcement learning (RL) agent that dynamically decides about using the LSTM prediction versus physical sensing in a way that maximizes energy saving while maintaining prediction accuracy. Two approaches are used for the RL: 1) the Markov decision process (MDP) model-based for low scale applications and 2) deep${Q}$-Network-based for larger scales. Compared to the current literature, the proposed solution is unique in predicting all sensor readings for real-time event detection and providing a model capable of learning long-term spatiotemporal correlations, enabling power conservation and detection accuracy balance. We compare the proposed solutions to the most relevant state-of-the-art approaches using a large real dataset collected in a dynamic space by measuring the accuracy, consumed energy, network lifetime, latency, and missed events’ ratio. To investigate the scalability of the solutions, these parameters are calculated for different network sizes. The results show that the system achieves 50% accuracy with 32% of activation time and 75% accuracy with 60% activation time.

The present paper considers emerging Internet of Things (IoT) applications and proposes a Long Short Term Memory (LSTM) based neural network for predicting the end of the broadcasting period under slotted CSMA (Carrier Sense Multiple Access) based MAC protocol and Energy Harvesting enabled Wireless Networks (EHWNs). The goal is to explore LSTM for minimizing the number of missed nodes and the number of broadcasting time-slots required to reach all the nodes under periodic broadcast operations. The proposed LSTM model predicts the end of the current broadcast period relying on the Root Mean Square Error (RMSE) values generated by its output, which (the RMSE) is used as an indicator for the divergence of the model. As a case study, we enhance our already developed broadcast policy, ADAPCAST by applying the proposed LSTM. This allows to dynamically adjust the end of the broadcast periods, instead of statically fixing it beforehand. An artificial data-set of the historical data is used to feed the proposed LSTM with information about the amounts of incoming, consumed, and effective energy per time-slot, and the radio activity besides the average number of missed nodes per frame. The obtained results prove the efficiency of the proposed LSTM model in terms of minimizing both the number of missed nodes and the number of time-slots required for completing broadcast operations.

Abstract This paper addresses the problem of responding to user queries by fetching the most relevant object from a clustered set of objects. It addresses the common drawbacks of cluster-based approaches and targets fast, high-quality information retrieval. For this purpose, a novel cluster-based information retrieval approach is proposed, named Cluster-based Retrieval using Pattern Mining (CRPM). This approach integrates various clustering and pattern mining algorithms. First, it generates clusters of objects that contain similar objects. Three clustering algorithms based on k-means, DBSCAN (Density-based spatial clustering of applications with noise), and Spectral are suggested to minimize the number of shared terms among the clusters of objects. Second, frequent and high-utility pattern mining algorithms are performed on each cluster to extract the pattern bases. Third, the clusters of objects are ranked for every query. In this context, two ranking strategies are proposed: i) Score Pattern Computing (SPC), which calculates a score representing the similarity between a user query and a cluster; and ii) Weighted Terms in Clusters (WTC), which calculates a weight for every term and uses the relevant terms to compute the score between a user query and each cluster. Irrelevant information derived from the pattern bases is also used to deal with unexpected user queries. To evaluate the proposed approach, extensive experiments were carried out on two use cases: the documents and tweets corpus. The results showed that the designed approach outperformed traditional and cluster-based information retrieval approaches in terms of the quality of the returned objects while being very competitive in terms of runtime.

This paper addresses the taxi fraud problem and introduces a new solution to identify trajectory outliers. The approach as presented allows to identify both individual and group outliers and is based on a two phase-based algorithm. The first phase determines the individual trajectory outliers by computing the distance of each point in each trajectory, whereas the second identifies the group trajectory outliers by exploring the individual trajectory outliers using both feature selection and sliding windows strategies. A parallel version of the algorithm is also proposed using a sliding window-based GPU approach to boost the runtime performance. Extensive experiments have been carried out to thoroughly demonstrate the usefulness of our methodology on both synthetic and real trajectory databases. The results show that the GPU approach enables reaching a speed-up of 341 over the sequential algorithm on large synthetic databases. The efficiency of the proposed method to detect both individual and group trajectory outliers on a real-world taxi trajectory database is also demonstrated in comparison with baseline trajectory outlier and group detection algorithms. The results are very promising and show superiority of the proposed method both in reducing computational time and enhancing the quality of returned outliers. Finally, we prime our methodology and results for future refinement using deep learning methodologies.

This article introduces two new problems related to trajectory outlier detection: (1) group trajectory outlier (GTO) detection and (2) deviation point detection for both individual and group of trajectory outliers. Five algorithms are proposed for the first problem by adapting DBSCAN , k nearest neighbors (kNN) , and feature selection (FS) . DBSCAN-GTO first applies DBSCAN to derive the micro clusters , which are considered as potential candidates. A pruning strategy based on density computation measure is then suggested to find the group of trajectory outliers. kNN-GTO recursively derives the trajectory candidates from the individual trajectory outliers and prunes them based on their density. The overall process is repeated for all individual trajectory outliers. FS-GTO considers the set of individual trajectory outliers as the set of all features, while the FS process is used to retrieve the group of trajectory outliers. The proposed algorithms are improved by incorporating ensemble learning and high-performance computing during the detection process. Moreover, we propose a general two-phase-based algorithm for detecting the deviation points, as well as a version for graphic processing units implementation using sliding windows. Experiments on a real trajectory dataset have been carried out to demonstrate the performance of the proposed approaches. The results show that they can efficiently identify useful patterns represented by group of trajectory outliers, deviation points, and that they outperform the baseline group detection algorithms.

Abstract This paper investigates the use of recurrent neural network to predict urban long-term traffic flows. A representation of the long-term flows with related weather and contextual information is first introduced. A recurrent neural network approach, named RNN-LF, is then proposed to predict the long-term of flows from multiple data sources. Moreover, a parallel implementation on GPU of the proposed solution is developed (GRNN-LF), which allows to boost the performance of RNN-LF. Several experiments have been carried out on real traffic flow including a small city (Odense, Denmark) and a very big city (Beijing). The results reveal that the sequential version (RNN-LF) is capable of dealing effectively with traffic of small cities. They also confirm the scalability of GRNN-LF compared to the most competitive GPU-based software tools when dealing with big traffic flow such as Beijing urban data.

Frequent Itemset Mining (FIM) is an important but very time-consuming data mining task. As a result, traditional FIM algorithms are often not scalable to large databases. To address this issue, several metaheuristics have been developed in recent years to find good approximate solutions to the FIM problem. It was shown that such approaches can be much more efficient than exact algorithms. However, metaheuristics often have long runtimes on massive datasets and the quality of their solutions can be improved. To address this issue, this paper proposes a parallel framework called CFIM (Cluster for Frequent Itemset Mining) for metaheuristic-based FIM. It accelerates FIM by using multiple cluster workers. The proposed approach partitions a transactional database and the set of all items at the level of cluster workers. The itemset generation process is performed by each worker, which then send results to a master node. This latter performs a merging step to only keep high quality itemsets by considering their frequency and diversification. Three metaheuristics (GA, PSO and BSO) are integrated in this framework to yield three novel metaheuristics (CGA, CPSO and CBSO). Extensive experiments show that CPSO outperforms CGA, CBSO, and state-of-the-art high performance computing FIM approaches.

This paper considers frequent itemset mining in big transactional databases. It first introduces a novel approach (Bio-SS) that combines the bio-inspired algorithms with the single scan algorithm (SSFIM). The proposed approach addresses the limitations of SSFIM by utilizing the bio-inspired operators in the generation process. This reduces the time complexity of SSFIM from exponential to polynomial, while taking advantage of the capacity to derive the frequent itemsets by performing a single database scan, independently from the minimum support value. This allows to considerably accelerate the scan procedure compared to existing approaches, especially when dealing with large scale databases. The numerical results show that the designed Bio-SS outperforms both accurate and metaheuristics baseline FIM approaches when dealing with big databases.

This paper presents a design and implementation of a data management platform to monitor and control smart objects in the Internet of Things (IoT). This is through IPv4/IPv6, and by combining IoT specific features and protocols such as CoAP, HTTP and WebSocket. The platform allows anomaly detection in IoT devices and real-time error reporting mechanisms. Moreover, the platform is designed as a standalone application, which targets at extending cloud connectivity to the edge of the network with fog computing. It extensively uses the features and entities provided by the Capillary Networks with a micro-services based architecture linked via a large set of REST APIs, which allows developing applications independently of the heterogeneous devices. The platform addresses the challenges in terms of connectivity, reliability, security and mobility of the Internet of Things through IPv6. The implementation of the platform is evaluated in a smart home scenario and tested via numeric results. The results show low latency, at the order of few ten of milliseconds, for building control over the implemented mobile application, which confirm realtime feature of the proposed solution.

Association Rule Mining (ARM) is a fundamental data mining task that is time-consuming on big datasets. Thus, developing new scalable algorithms for this problem is desirable. Recently, Bee Swarm Optimization (BSO)-based meta-heuristics were shown effective to reduce the time required for ARM. But these approaches were applied only on small or medium scale databases. To perform ARM on big databases, a promising approach is to design parallel algorithms using the massively parallel threads of a GPU processor. While some GPU-based ARM algorithms have been developed, they only benefit from GPU parallelism during the evaluation step of solutions obtained by the BSO-metaheuristics. This paper improves this approach by parallelizing the other steps of the BSO process (diversification and intensification). Based on these novel ideas, three novel algorithms are presented, i) DRGPU (Determination of Regions on GPU), ii) SAGPU (Search Area on GPU, and, iii) ALLGPU (All steps on GPU). These solutions are analyzed and empirically compared on benchmark datasets. Experimental results show that ALLGPU outperforms the three other approaches in terms of speed up. Moreover, results confirm that ALLGPU outperforms the state-of-the-art GPU-based ARM approaches on big ARM databases such as the Webdocs dataset. Furthermore, ALLGPU is extended to mine big frequent graphs and results demonstrate its superiority over the state-of-the-art D-Mine algorithm for frequent graph mining on the large Pokec social network dataset.

We explore in this paper the application of bioinspired approaches to the association rules mining (ARM) problem for the purpose of accelerating the process of extracting the correlations between items in sizeable data instances. A new bio-inspired GPU-based model is proposed, which benefits from the massively GPU threading by evaluating multiple rules in parallel on GPU. To validate the proposed model, the most used bio-inspired approaches (GA, PSO, and BSO) have been executed on GPU to solve wellknown large ARM instances. Real experiments have been carried out on an Intel Xeon 64 bit quad-core processor E5520 coupled to an Nvidia Tesla C2075 GPU device. The results show that the genetic algorithm outperforms PSO and BSO. Moreover, it outperforms the state-of-the-art GPU-based ARM approaches when dealing with the challenging Webdocs instance.

Summary The association rules mining (ARM) problem is one of the most important problems in the area of data mining. It aims at finding all relevant association rules from transactional databases. It is CPU time intensive and requires a huge computing power when dealing with large transactional databases. To deal with this issue, Graphics Processing Units (GPUs) are a powerful tool to speed up the search process. However, their performance is closely subject to thread/branch divergence resulting from the single instruction multiple data parallel model of GPUs. In this paper, we propose three approaches based on database reorganization, aiming to reduce thread divergence in GPU‐based bees swarm optimization metaheuristic for ARM, respectively, named block‐based reordering, transactions‐based reordering, and transactions‐based reordering with median value. Theoretical and experimental studies have been carried out using well‐known large ARM instances. The experiments have been performed on an Intel Xeon 64 bit quad‐core processor E5520 coupled to Nvidia Tesla C2075 448 cores. The results show that the proposed approaches minimize considerably the number of thread divergence and improve the overall execution time. Indeed, the number of thread divergence occurrences has been reduced by up to eight times making the execution much faster. Copyright © 2016 John Wiley & Sons, Ltd.

This paper considers the problem of communication coverage for sustainable data forwarding in wireless sensor networks, where an energy-aware deployment model of relay nodes (RNs) is proposed. The model used in this paper considers constrained placement and is different from the existing one-tiered and two-tiered models. It supposes two different types of sensor nodes to be deployed, energy rich nodes (ERNs), and energy limited nodes (ELNs). The aim is thus to use only the ERNs for relaying packets, while ELN's use will be limited to sensing and transmitting their own readings. A minimum number of RNs is added if necessary to help ELNs. This intuitively ensures sustainable coverage and prolongs the network lifetime. The problem is reduced to the traditional problem of minimum weighted connected dominating set (MWCDS) in a vertex weighted graph. It is then solved by taking advantage of the simple form of the weight function, both when deriving exact and approximate solutions. Optimal solution is derived using integer linear programming (ILP), and a heuristic is given for the approximate solution. Upper bounds for the approximation of the heuristic (versus the optimal solution) and for its runtime are formally derived. The proposed model and solutions are also evaluated by simulation. The proposed model is compared with the one-tiered and two-tiered models when using similar solution to determine RNs positions, i.e., minimum connected dominating set (MCDS) calculation. Results demonstrate the proposed model considerably improves the network life time compared to the one-tiered model, and this by adding a lower number of RNs compared to the two-tiered model. Further, both the heuristic and the ILP for the MWCDS are evaluated and compared with a state-of-the-art algorithm. The results show the proposed heuristic has runtime close to the ILP while clearly reducing the runtime compared to both ILP and existing heuristics. The results also demonstrate scalability of the proposed solution.

This paper tackles the challenge of optimally placing relay nodes (RNs) in wireless sensor networks given a limited set of positions. The proposed solution consists of: (1) the usage of a realistic physical layer model based on a Rayleigh block-fading channel; (2) the calculation of the signal-to-interference-plus-noise ratio (SINR) considering the path loss, fast fading, and interference; and (3) the usage of a weighted communication graph drawn based on outage probabilities determined from the calculated SINR for every communication link. Overall, the proposed solution aims for minimizing the outage probabilities when constructing the routing tree, by adding a minimum number of RNs that guarantee connectivity. In comparison to the state-of-the art solutions, the conducted simulations reveal that the proposed solution exhibits highly encouraging results at a reasonable cost in terms of the number of added RNs. The gain is proved high in terms of extending the network lifetime, reducing the end-to-end- delay, and increasing the goodput.

Optimizing energy consumption and end-to-end (e2e) packet delay in energy-constrained, delay-sensitive wireless sensor networks is a conflicting multiobjective optimization problem. We investigate the problem from a game theory perspective, where the two optimization objectives are considered as game players. The cost model of each player is mapped through a generalized optimization framework onto protocol-specific MAC parameters. From the optimization framework, a game is first defined by the Nash bargaining solution (NBS) to assure energy consumption and e2e delay balancing. Secondy, the Kalai-Smorodinsky bargaining solution (KSBS) is used to find an equal proportion of gain between players. Both methods offer a bargaining solution to the duty-cycle MAC protocol under different axioms. As a result, given the two performance requirements (i.e., the maximum latency tolerated by the application and the initial energy budget of nodes), the proposed framework allows to set tunable system parameters to reach a fair equilibrium point that dually minimizes the system latency and energy consumption. For illustration, this formulation is applied to six state-of-the-art wireless sensor network (WSN) MAC protocols: B-MAC, X-MAC, RI-MAC, SMAC, DMAC, and LMAC. The article shows the effectiveness and scalability of such a framework in optimizing protocol parameters that achieve a fair energy-delay performance trade-off under the application requirements.

This paper deals with power-efficient coverage in wireless sensor networks (WSN) by taking advantage of energy-harvesting capabilities. A general scenario is considered for deployed networks with two types of sensor nodes, harvesting enabled nodes (HNs), and none-harvesting nodes (NHNs). The aim is to use only the HNs for relaying packets, while NHNs use will be limited to sensing and transmitting their own readings. The problem is modeled using graph theory and reduced to finding the minimum weighted connected dominating set in a vertex weighted graph. A limited number of relay nodes is added at the positions close to the NHNs in the resulted set. The weight function ensures minimizing the number of NHNs in the set, and thus reducing the relay nodes to be added. Our contribution is to consider relay node placement (addition) in energy harvesting WSN, where only HNs are used to forward packets. This is to preserve the limited energy of NHNs. Extensive simulation results show that the proposed relay node addition strategy prolongs the network lifetime, from the double, to factors of several tens of times. This is at a reasonable cost in terms of the number of relay nodes added, which is compared to a lower-bound derived in the paper.

This article considers the data aggregation scheduling problem, where a collision-free schedule is determined in a distributed way to route the aggregated data from all the sensor nodes to the base station within the least time duration. The algorithm proposed in this article (Distributed algorithm for Integrated tree Construction and data Aggregation (DICA)) intertwines the tree formation and node scheduling to reduce the time latency. Furthermore, while forming the aggregation tree, DICA maximizes the available choices for parent selection at every node, where a parent may have the same, lower, or higher hop count to the base station. The correctness of the DICA is formally proven, and upper bounds for time and communication overhead are derived. Its performance is evaluated through simulation and compared with six delay-aware aggregation algorithms. The results show that DICA outperforms competing schemes. The article also presents a general hardware-in-the-loop framework (DAF) for validating data aggregation schemes on Wireless Sensor Networks (WSNs). The framework factors in practical issues such as clock synchronization and the sensor node hardware. DICA is implemented and validated using this framework on a test bed of sensor motes that runs TinyOS 2.x, and it is compared with a distributed protocol (DAS) that is also implemented using the proposed framework.

Optimizing energy consumption and end-to-end (e2e) packet delay in energy constrained distributed wireless networks is a conflicting multi-objective optimization problem. This paper investigates this trade-off from a game-theoretic perspective, where the two optimization objectives are considered as virtual game players that attempt to optimize their utility values. The cost model of each player is mapped through a generalized optimization framework onto protocol specific MAC parameters. A cooperative game is then defined, in which the Nash Bargaining solution assures the balance between energy consumption and e2e packet delay. For illustration, this formulation is applied to three state-of-the-art wireless sensor network MAC protocols; X-MAC, DMAC, and LMAC as representatives of preamble sampling, slotted contention-based, and frame-based MAC categories, respectively. The paper shows the effectiveness of such framework in optimizing protocol parameters for achieving a fair energy-delay performance trade-off, under the application requirements in terms of initial energy budget and maximum e2e packet delay. The proposed framework is scalable with the increase in the number of nodes, as the players represent the optimization metrics instead of nodes.

Receiver-to-receiver time synchronization in wireless networks is considered, and appropriate maximum-likelihood estimators (MLE) for environments with exponential distrusted reception delays are proposed. In the receiver-to-receiver synchronization approach, time at receivers should be directly related to one another without referring to the sender (reference), which permits to eliminate the sender's uncertainty from the variable delays (time critical-path). The models and estimators proposed for the sender-to-receiver approach are thus inappropriate for the receiver-to-receiver one. A model that accurately reflects the relative feature of the considered approach and eliminates the senders's uncertainty is used, where timestamps at the receivers are directly related without referring to the sender's time or timestamps. By directly relating time at two receivers with identical exponential reception delay, Exp(λ), it yields a Laplace(0,1/λ) distribution as the difference between the two delays. By using the log-likelihood function of the latter and the ML method, the offset estimator is analytically derived and a linear program is given for the joint offset/skew model. The accuracy of the proposed estimators has been numerically analyzed by simulation. Results show high precision of the proposed estimators, which can be integrated with any receiver-to-receiver synchronization protocol.

In wireless sensor networks, the sensor data are often aggregated en-route to the base-station in order to eliminate redundancy and conserve the network resources. The basestation not only acts as a destination for the upstream data traffic, but it also configures the network by transmitting commands downstream to nodes. The data delivery latency is a critical performance metric in time-sensitive applications and is considered by a number of data aggregation schemes in the literature. However, to the best of our knowledge, no solution has considered the scheduling of downstream packets, originated from the base-station, in conjunction with upstream data aggregation traffic. This paper fills such a gap and proposes MASAUD, which intertwines the medium access schedule of upstream and downstream traffic in order to reuse time slots in a non-conflicting manner and reduce delay. MASAUD can be integrated with any scheme for data aggregation scheduling. The simulation confirms the effectiveness of MASAUD.

The real-world node deployment aspect is investigated, while considering cost minimization for resolving the energy hole around the sink, which represents a serious problem in typical sensor networks with uniform distribution. A novel strategy is proposed that is based on the use of two sinks and a few extra relay nodes close to the sinks' areas. The traffic is then alternatively sent to the sinks in every other cycle. As a second contribution, an efficient data collection mechanism has been developed to determine the optimal data rate that meets delay requirements of individual sensor reports and improves the network lifetime. The comparison of the proposed node deployment strategy with uniform, non-uniform geometric and linear increase node distributions demonstrates that the cost of the proposed solution is very close to that of the uniform distribution and much lower than all the others, while achieving a load balancing at the same order of the state-of-the-art solutions.

We present Duo-MAC, an asynchronous cascading wake-up scheduled MAC protocol for heterogeneous traffic forwarding in low-power wireless networks. Duo-MAC deals with energy-delay minimization problem and copes with transmission latency encountered by Today's duty-cycled protocols when forwarding heterogeneous traffic types. It switches, according to the energy and delay requirements, between Low Duty cycle (LDC) and High Duty Cycle (HDC) operating modes, and it quietly adjusts the wake-up schedule of a node according to (i) its parent's wake-up time and (ii) its estimated load, using an effective real-time signal processing linear traffic estimator. As a second contribution, Duo-MAC, proposes a service differentiation through an improved contention window adaptation algorithm to meet delay requirements of heterogeneous traffic classes. Duo-MAC's efficiency stems from balancing between the two traffic award operation modes. Implementation and experimentation of Duo-MAC on a MicaZ mote platform reveals that the protocol outperforms other state-of-the-art MAC protocols from the energy-delay minimization perspective.

Network management is revisited in the emerging ubiquitous sensor networks (USNs) that form the Internet-of-the-Things (IoT) with the objective of evaluating the impact of traffic engineering on energy efficiency and assessing if routing simplicity translates into scalability. USN management is formulated as a local optimization problem minimizing the number of traffic flows transiting by a node: the nodes traffic flow interference with other nodes. The least interference beaconing algorithm (LIBA) is proposed as an algorithmic solution to the problem, and the least interference beaconing protocol (LIBP) as its protocol implementation. LIBP extends the beaconing process widely used by collection protocols with load balancing to improve the USN energy efficiency. Simulation results reveal the relative efficiency of the resulting traffic engineering scheme compared to state of the art protocols. These results show up to 30% reduction in power consumption compared to TinyOS beaconing (TOB), and up to 40% compared to collection tree protocol (CTP) while sustaining better performance in terms of scalability.

This paper describes the implementation and evaluation of R4Syn protocol on MICAz platform and TinyOS operating system. The contribution is two folds. First, the implementation uses thorough maximum-likelihood estimators (MLE) in the joint offset/skew model, while all similar MLEbased estimators are merely evaluated with theoretical and numerical analysis thus far, and empirical solutions use simple computation estimators, such as offset-only models, or linear regression for skew estimation. Difficulties that has been encountered and overcome are reported in this paper. The second contribution is to consider fault-tolerance, an aspect that has been completely abstracted in previous works. The implementation assures correct behavior despite nodes failure or packet loss, as demonstrated by the experiments. Experimental results also demonstrate microsecond-level precision and long-term validity of the estimators in the joint skew/offset model.

This paper considers slotted duty-cycled medium access control (MAC) protocols, where sensor nodes periodically and synchronously alternate their operations between active and sleep modes to save energy. Communications can occur only when nodes are in active mode. The synchronous feature makes these protocols more appropriate for delay-sensitive applications than asynchronous protocols. With asynchronous protocols, additional delay is needed for the sender to meet the receiver's active period. This is eliminated with synchronous approaches, where nodes sleep and wake up all together. Moreover, the contention-based feature makes the protocols - considered in this paper - conceptually distributed and more dynamic compared to TDMA protocols. Duty cycling allows obtaining significant energy saving vs. full duty cycle (sleepless) protocols. However, it may result in significant latency. Forwarding a packet over multiple hops often requires multiple operational cycles (sleep latency), i.e. nodes have to wait for the next cycle to forward data at each hop. Timeliness issues of slotted contention-based MAC protocols are dealt with in this paper, where a comprehensive review and taxonomy is provided. The main contribution is to study and classify the protocols from the delay-efficiency perspective.

This paper proposes a cluster-based broadcast protocol to disseminate delay-sensitive information throughout a wireless sensor network (WSN). The protocol considers the use of duty-cycling at the MAC layer, which is essential to reduce energy dissipation. LEACH's energy-efficiency approach is used for cluster construction. The proposed protocol adds new common static and dynamic broadcast periods to support and accelerate broadcasting. The dynamic periods are scheduled following the past arrivals of messages, and using a Markov-chain model. To our knowledge, this work is the first that proposes the use of clustering to reduce broadcast latency. The clustering mechanism allows for simultaneous local broadcasts at several clusters in the WSN, and it also ensures scalability with the increase of the network size. The protocol has been simulated, numerically analyzed, and compared with LEACH. The results show clear improvement over LEACH with regard to the latency.

A new time synchronization protocol for wireless sensor networks (WSN) is proposed. It uses the receiver-to-receiver principle introduced by the Reference Broadcast Synchronization (RBS), which reduces the time-critical path compared to the sender-to-receiver approach. The proposed protocol has the advantage of distributing the reference's function among all sensors, which eliminates the single point of failure (reference) shortcomings of RBS. It also allows timestamps to be piggybacked to the regular signals (beacons) and thus eliminates the need of separate transmissions for exchanging timestamps. After local synchronization, a multihop extension is proposed using final local estimates, with no forwarding of synchronization signals. Maximum likelihood estimators (MLE) are derived to estimate relative skew/offset for channels with Gaussian distributed delays. The Cramer-Rao lower bounds (CRLB) are accordingly derived and numerically compared with the MLE's mean square error (MSE). Results show convergence of the proposed estimators' precision to their respective CRLB with the increase of the number of signals.

A new localized quality of service (QoS) routing protocol for wireless sensor networks (WSN) is proposed in this paper. The proposed protocol targets WSN's applications having different types of data traffic. It is based on differentiating QoS requirements according to the data type, which enables to provide several and customized QoS metrics for each traffic category. With each packet, the protocol attempts to fulfill the required data-related QoS metric(s) while considering power efficiency. It is modular and uses geographical information, which eliminates the need of propagating routing information. For link quality estimation, the protocol employs distributed, memory and computation efficient mechanisms. It uses a multisink single-path approach to increase reliability. To our knowledge, this protocol is the first that makes use of the diversity in data traffic while considering latency, reliability, residual energy in sensor nodes, and transmission power between nodes to cast QoS metrics as a multiobjective problem. The proposed protocol can operate with any medium access control (MAC) protocol, provided that it employs an acknowledgment (ACK) mechanism. Extensive simulation study with scenarios of 900 nodes shows the proposed protocol outperforms all comparable state-of-the-art QoS and localized routing protocols. Moreover, the protocol has been implemented on sensor motes and tested in a sensor network testbed.

In this paper we deal with biomedical applications of wireless sensor networks, and propose a new quality of service (QoS) routing protocol. The protocol design relies on tra±c diversity of these applications and en- sures a di®erentiation routing using QoS metrics. It is based on modular and scalab

This paper proposes a multi-objective quality of service (QoS) routing protocol for wireless sensor networks (WSN). The protocol takes into account the traffic diversity typical for many applications and provides a differentiation in routing using QoS metrics. It ensures several QoS metrics for different traffic categories, and attempts for each packet to fulfill the required metrics in a power-aware and localized way. It employs memory and computation efficient estimators in a distributed manner and uses a multi-sink single-path approach to increase reliability. The main contribution of this paper is data traffic based QoS with regard to all the considered QoS metrics. As far as we know, this protocol is the first that makes use of the diversity in the data traffic while considering latency, reliability, residual energy in the sensor nodes, and transmission power between nodes and casts QoS metrics as a multi-objective problem. The proposed algorithm can operate with any MAC protocol, provided that it employs an ACK mechanism. Simulation results show the proposed protocol outperforms all compared state-of-the-art QoS and localized routing protocols.

Abstract Since mobile ad hoc networks (MANETs) are infrastructureless and multi‐hop by nature, transmitting packets from any node to another usually relies on services provided by intermediate nodes. This reliance introduces a new vulnerability; one node could launch aBlack Hole DoS attackby participating in the routing protocol and including itself in routes, then simply dropping packets it receives to forward. Another motivation for dropping packets in self‐organized MANETs is resource preservation. Some solutions for detecting and isolating packet droppers have been recently proposed, but almost all of them employ the promiscuous mode monitoring approach (watchdog (WD)) which suffers from many problems, especially when employing the power control technique. In this paper we propose a novel monitoring approach that overcomes some WD's shortcomings, and improves the efficiency in detection. To overcome false detections due to nodes mobility and channel conditions we propose a Bayesian technique for the judgment, allowing node redemption before judgment. Finally, we suggest a social‐based approach for the detection approval and isolation of guilty nodes. We analyze our solution and asses its performance by simulation. The results illustrate a large improvement of our monitoring solution in detection versus the WD, and an efficiency through our judgment and isolation techniques as well. Copyright © 2007 John Wiley & Sons, Ltd.

One of the commonest threats that mobile ad hoc networks are vulnerable to is data packet dropping, which is caused either by malicious or selfish nodes. Most of the existing solutions to solve such misbehaviour rely on the watchdog technique, which suffers from many drawbacks, particularly when using the power control technique. To overcome this problem with a moderate communication overhead, this paper introduces a new approach for detecting misbehaving nodes that drop data packets in MANET. It consists of two stages the monitoring stage in which each node monitors its direct neighbours with respect to forwarding data packets of a traffic session in the network, and the decision stage, in which direct neighbouring nodes decide whether the monitored node misbehave or not. Our new approach is able to detect the misbehaviour in case of power control employment, with a low communication overhead compared to the existing approaches.