Machine Learning (ML) malware detectors rely heavily on crowd-sourced AntiVirus (AV) labels, with platforms like VirusTotal serving as trusted sources of malware annotations. But what if attackers could manipulate these labels to classify benign software as malicious? We introduce label spoofing attacks, a new threat that contaminates crowd-sourced datasets by embedding minimal and undetectable malicious patterns into benign samples. These patterns coerce AV engines into misclassifying legitimate files as harmful, enabling poisoning attacks against ML-based malware classifiers trained on those data. We demonstrate this scenario by developing AndroVenom, a methodology for polluting realistic data sources and launching subsequent poisoning attacks against ML malware detectors. Experiments show that not only are state-of-the-art feature extractors unable to filter such injections, but various ML models experience Denial-of-Service (DoS) with as little as 1% poisoned samples. Additionally, attackers can flip decisions for specific unaltered benign samples by modifying only 0.015% of the training data, threatening their reputation and market share, while evading anomaly detectors operating on the training data. We conclude by raising concerns about the trustworthiness of ML training processes based on AV annotations and argue that further investigation is needed to develop more reliable labeling strategies.

We present PEARL (Plasticity-Enhanced Adaptive Responsive Learning), a novel framework that enables low-latency federated learning using plasticity-driven spiking neural networks (SNNs) on resource-constrained edge devices. PEARL combines a computationally efficient variant of Spike-Timing-Dependent Plasticity (STDP) with an online learning mechanism that supports both sample-wise and mini-batch updates. A standard Federated Averaging (FedAvg) algorithm is used to coordinate global model updates across distributed clients while preserving data privacy.While previous work has explored the integration of SNNs into federated learning, few approaches address the stringent latency requirements of real-time edge intelligence. PEARL bridges this gap by coupling biologically inspired, event-driven learning with efficient distributed optimization, significantly accelerating convergence without sacrificing accuracy.Experiments on the MNIST dataset demonstrate that PEARL reduces the time to reach 80% accuracy by up to 66.7% com-pared to state-of-the-art methods, while consistently achieving higher final accuracy across a range of client configurations. These results establish PEARL as an effective and scalable solution for latency-sensitive neuromorphic learning in distributed edge environments.

This paper introduces Time-Introduced baCKpropagation Through Optimized Cooperation (TICKTOC), a real-time approach to improve signal transmission that integrates spiking neural networks (SNNs) with Cooperative Optimization (CO-OP) from asynchronous federated learning, enabling dynamic adjustments in digital predistortion (DPD). SNNs offer energy-efficient processing suited to the power demands of 5G/6G infrastructure. Federated learning enables faster decentralized updates through inherent parallelism, while CO-OP further reduces latency by using asynchronous aggregation, allowing faster updates and improving real-time performance when accommodating heterogeneous client workloads. Simulation results demonstrate that TICKTOC achieves a 50.0% faster convergence compared to Centralized BPTT and 75.0% compared to FedAvg BPTT, significantly outperforming state-of-the-art SNN methods in real-time DPD applications.

Uncrewed aerial vehicles (UAVs) are becoming essential in various sectors, such as commercial delivery, agricultural monitoring, and disaster response. Despite their benefits, the rapid adoption of UAVs poses substantial security challenges, especially in drone network intrusion detection. Traditional intrusion detection datasets often suffer from limitations like small sample sizes and uneven distribution, undermining the effectiveness of intrusion detection systems. Moreover, conventional machine learning (ML) approaches generally require extensive, well-labeled datasets that are expensive and labor intensive to produce. To overcome these challenges, we introduce a generative adversarial network (GAN) model designed to enhance and balance the limited datasets available for drone networks. This model significantly improves data quality and quantity, thus optimizing the training process for intrusion detection models. Furthermore, we propose a Human-in-the-Loop (HITL) ML framework that integrates human expertise to guide the learning process and mitigate the costs of labeling. Our comprehensive evaluation demonstrates that the combined application of the GAN model and the HITL framework significantly outperforms traditional baseline models. This approach not only achieves an intrusion detection accuracy of up to 99% across various experimental datasets but also dramatically reduces the requirement for large amounts of labeled data by up to 98%, providing a cost-effective solution for enhancing UAV network security.

Due to their usefulness in smart cities and other civilian uses, drones are becoming increasingly popular. With the ability to be organized into networks, they can be used to gather various kinds of data, including images and videos with multimedia characteristics, and then forward it to processing centers for additional handling. They also become a fresh target for a variety of attacks, such as GPS spoofing, denial of service, and false data injection. It is therefore vital and required to create new systems and protection mechanisms against these threats. In this research, we highlight the risk associated with the so-called False Data Injection (FDI) and present a deep learning-based approach for detecting it. An injection of misleading data into the data (images) gathered by the drones is regarded as a serious and potent attack that has the potential to significantly change a final judgment made by the processing center. Our strategy uses deep learning for image analysis and classification in order to thwart this attack. Using Nearest Neighbor Interpolation (NNI) to scale the incoming image to match the classifier, we next feed the image to a Convolutional Neural Network (CNN) for image classification. Finally, we use the Mahalanobis Distance to compare each class of classification results to a neighborhood. Our solution performs well, irrespective of image size, as evidenced by numerical findings on the current dataset, which show an accuracy of 97.71%, a precision of 96.69%, a recall of 94.33%, and an F-score of 0.941%.

Initial Coin Offerings (ICOs) have emerged as a groundbreaking method for blockchain startups to raise capital, allowing projects to generate significant funding through the issuance of digital tokens. Despite their potential to raise millions, often outpacing traditional approaches like Initial Public Offerings (IPOs), ICOs remain high-risk investments due to limited regulatory oversight and a lack of standardization. This study investigates the impact of human capital on ICO success, employing machine learning techniques, including advanced boosting algorithms, to enhance predictive accuracy. Using data from 500 ICOs and analyzing over 5,524 LinkedIn profiles of project team members and advisors, we demonstrate that human capital factors can substantially inform success predictions. Our model achieves a notable 72% prediction accuracy, offering valuable insights amid the inherent volatility and unpredictability of ICO markets. These findings underscore the role of human capital in ICO outcomes, providing a data-driven framework for stakeholders to assess ICO prospects.

Drones, also called unmanned aerial vehicles (UAV), have become essential for surveillance tasks like warning systems, wildfire monitoring, and precision agriculture. To increase efficiency and energy savings, drone fleets are now prioritized over individual units. However, coordinating these fleets poses a challenge. Existing methods generally use a centralized control system for coordination and identification of events. In this paper, we introduce a cooperative and autonomous flocking method for drone piloting. This method mimics the behaviors of flocks of birds using a network coordination protocol and wireless communications. The foundation of this approach is the BOID model, which is based on three rules: separation, alignment and cohesion. Here, we improve this model by integrating detection and relative localization capabilities, resulting in the formation of dynamic and adaptable herds. The experiments carried out in simulation of our technique prove its ability to improve the performance of data exchanges and make routing more reliable, thus potentially improving the use of drones in various industries and needs.

Drone swarm coordination, inspired by natural swarm behaviors such as bird flocking, has traditionally hinged on rule-based methodologies, with Craig Reynolds' Boid algorithm as a seminal reference. These rule-based strategies, although functional in many contexts, fail to capture the intricate adaptive learning mechanisms inherent to birds navigating dynamic environments. Recognizing this limitation, this paper introduces a multi-agent reinforcement learning (MARL) approach to Boid modeling for drone swarms, its more authentic emulation of the nuanced learning processes witnessed in avian species. Our methodology leverages reinforcement learning algorithms to train individual drones, enabling them to autonomously make decisions based on their local environment and the overall swarm's state. Distinct from traditional rule-based models, our MARL-driven drones continually optimize their collective and individual behaviors, echoing the adaptability of natural flocks. This adaptability, intrinsically woven into the fabric of our model, offers heightened proficiency in navigating complex, mutable environments, bringing the simulation closer to the organic flocking dynamics observed in nature. Empirical evaluations showcase that our MARL Boid not only surpass traditional rule-based models in cohesion, alignment, and separation but also excel in adaptability to environmental variations. Moreover, our model is able to capture the flocking behavior quite effectively and show robustness against external perturbations.

The emergence of Unmanned Aerial Vehicle (UAV) networks has brought about intricate security challenges, especially in the domain of intrusion detection. At the same time, traditional machine learning algorithms for network security are becoming progressively inadequate, particularly when confronted with adversarial attacks and advanced persistent threats. Adding to the complexity, real-world UAV data streams are relentless, extensive, and demand substantial storage, making the storage of all data almost unfeasible. Furthermore, a significant gap in research lies in the lack of universally recognized datasets specifically designed for UAV network intrusion detection. Taking into account the aforementioned factors, this paper introduces an innovative approach for real-time intrusion detection. This approach harnesses Human-in-the-Loop Machine Learning (HITL-ML) to integrate human expertise directly into the machine learning process. By doing so, it enhances the system's capacity to adapt to evolving threats. Additionally, we introduce Generative Adversarial Networks (GANs) to synthetically generate data that mimics authentic network intrusions, thereby addressing the issue of limited dataset availability. This integration substantially enhances detection accuracy and reduces false positives, signifying a remarkable leap forward in contrast to conventional detection systems.

Healthcare sensor authentication can be challenging due to the limited energy capacity and small size of the devices. The need for additional hardware, such as NFC or displays, for secure pairing, as imposed by the Bluetooth Low Energy (BLE) standard, further worsens this challenge. One promising alternative for node authentication is the use of Radio Frequency Fingerprint Identification (RFFI). We investigate RFFI’s effectiveness within Wireless Body Area Networks (WBANs) using the real-world BLE-WBAN dataset, which includes signals emitted by body-mounted BLE sensors inside an anechoic chamber. Diverging from most prior research, our approach leverages higher fidelity input (real and imaginary parts together) in Complex-Valued Neural Networks (CV-NNs), despite their increased computational cost, to realize a substantial improvement in fingerprinting accuracy over traditional Real-Valued Neural Networks (RV-NN). Our experiments demonstrate that CV-NNs excel, particularly at higher sampling rates, and reveal that the human body can positively influence RFFI, achieving up to 88% accuracy in on-body node fingerprinting among 12 nodes. While our results are promising, they highlight the need for further validation to confirm CV-NNs’ potential to bolster RFFI in WBANs.

Vehicle ad-hoc networks represent a promising technology aimed at providing more efficient, safer, and partially autonomous transportation through vehicle-to-vehicle and infrastructure communications. The success of these networks heavily relies on the integrity and correctness of transmitted information. One of the most significant threats to communication is Denial of Service attacks, which can disrupt the system by overloading hardware modules. To effectively identify these attacks, we propose a modified version of the online K-Means algorithm. In our method, we aim to mitigate the impact of outliers on each adjusted centroid by incorporating an outlier detection method. In this paper, we compare the performance of the model by incorporating 4 different outlier detection methods in terms of execution time, accuracy and required buffer sizes. Additionally, we address a common oversight in accuracy calculations when using buffers for streaming data. Our study highlights the impact of data loss on accuracy. We also suggest several techniques to enhance the model in detecting DoS Attacks in terms of accuracy, precision and F1-score.

In contrast to artificial neural networks (ANNs), spiking neural networks (SNNs), referred to as third-generation neural networks, exhibit behaviors that closely resemble those of the human brain. They are believed to offer more advantages in terms of energy efficiency and performance. Numerous training algorithms have surfaced for SNN, primarily falling into three main categories: ANN-to-SNN (ANN2SNN) algorithms, backpropagation through time (BPTT) algorithms, and spike-timing-dependent plasticity (STDP) algorithms. Among these, ANN2SNN algorithms lack temporal dependency, whereas BPTT and STDP algorithms do incorporate temporal aspects. In this study, we utilize two datasets to compare the performance of these three types of algorithms: the neuromorphic MNIST dataset with a time dimension and the conventional MNIST dataset without this temporal aspect. The comparison is conducted considering classification accuracy, dataset adaptability, and data dependency. The experimental findings indicate that the ANN2SNN algorithm is not compatible with the neuromorphic MNIST dataset. Moreover, the temporal-dependent BPTT and STDP algorithms notably outperform ANN2SNN in terms of data dependency. The connection between the temporal dependency of algorithms and their performance is further discussed. Moreover, the paper provides valuable insights into future research avenues and practical applications of SNNs.

The lack of availability of real-world RF datasets has often impeded physical layer research relating to IoT and Health IoT sectors. Towards this end, this paper presents an unprecedented open-source Bluetooth Low Energy (BLE) dataset, encompassing 100 MSps radio signal recordings from 13 ESP32 IoT nodes in both on-body and over-the-air settings within an anechoic chamber, utilizing the dual receiver setup on USRP x310. Wideband (complete 2.4 GHz ISM band) acquisition and high sampling frequency ensure exceptional resolution and room for use case-specific sampling scalability in the dataset. We delineate the dataset's acquisition methodology, spotlight its inherent advantages, and offer a valuable Python tool to process and scrutinize the raw IQ samples. To facilitate the use of datasets in machine learning applications for the physical layer, we have incorporated this tool within a dataset hosted on a public GitHub repository. Here, we explore preliminary feature selection and conduct tests and training of machine learning models, including Random Forest and K-Nearest Neighbors (KNN) that achieve 81% accuracy in node classification for static on-body frames. This demonstrates the dataset's versatility in various scenarios, such as authentication and localization.

The primary objective of this study is to introduce an innovative framework for privacy-preserving federated learning (FL) by utilizing distributed differential privacy through secure aggregation (DDP-SA) to tackle the issue of privacy breaches during the FL training phase. Recent studies have demonstrated that traditional FL methods do not offer adequate privacy assurances, as malicious entities can deduce sensitive information about the training data from local model parameters or gradients, known as privacy inference attacks. The core concept behind DDP-SA is to integrate local differential privacy (LDP) and secure multi-party computation (MPC) to safeguard the gradients of local clients throughout the FL training process. Comprehensive experiments have been conducted to demonstrate that the proposed DDP-SA approach can deliver enhanced privacy safeguards in the FL process in comparison to either a solely LDP or solely MPC approach, while still maintaining a reasonable level of efficiency and accuracy.

Vehicular Networks enable vehicles to establish communication with both other vehicles and fixed road site units for the purpose of sharing critical safety and road traffic information. These networks contribute to the development of a self-aware, efficient, secure, and partially autonomous transportation system by facilitating the exchange and collection of Cooperative Awareness Messages (CAMs). However, vehicular networks are vulnerable to security attacks, where one or more vehicles may flood neighboring vehicles with malicious CAM messages. To effectively detect such attacks, an unsupervised online learning approach, such as our proposed modified version of the online K-means algorithm, can be employed. We evaluate this method using seven datasets, achieving nearly optimal detection performance. Our approach outperforms both the traditional online K-means algorithm and three other clustering methods, yielding higher accuracy and F1-scores.

This work proposes a Fog-enabled Mobile Sensor (FoMS) virtualization platform for serving Internet of Things (IoT) applications. Traditionally, a Mobile Sensor-Cloud (MSC) architecture uses the concept of mobile sensor virtualization and provisions mobile Sensors-as-a-Service (mSe-aaS) to serve multiple IoT applications. However, the existing architecture of MSC does not consider time-critical IoT applications such as transportation, industry, and rescue management. Therefore, considering these time-critical applications, we propose the FoMS architecture, which is capable of reducing overall service latency for an application. In FoMS, we adopt the inherent characteristics - of handling service latency - of fog computing in MSC architecture to provide virtualized mSe-aaS in the stipulated time. As fog computing is new to an MSC architecture, the existing fog node selection schemes are unsuitable for FoMS. Therefore, we design a scheme for selecting a suitable fog node for FoMS and proposed an optimal pricing strategy for the selected fog node. We depict the effects on the utility and price for the different parameters we considered.

Cooperative Intelligent Transport Systems (C-ITS) Deployment Platform is considered the newest version of vehicular communication systems, which enables the cooperation between two or more ITS sub-systems to provide enhanced services. With the expanded communication range and system complexity, ensuring the credibility of access nodes and protecting users from being monitored has become a difficult problem in network security, especially the services provided by remote servers like navigation. Transport Layer Security (TLS) is widely used for user authentication and encrypted data transmission in all networks. However, although the TLS handshake complexity is significantly reduced in TLS 1.3 the transmission of a full certificate chain during the handshake is still costly, especially for high-mobility vehicles. In this paper, we propose an optional extension named Certificate Get to reduce the TLS handshake overhead in C-ITS. Specifically, with our proposed extension, the revisiting client transmits a hash value of the certificate chain corresponding to a certain server in the ClientHello message, which can reduce the transmission payload of the certificate chain from an average of 4874 bytes to 68 bytes. Simulation results show that our proposed scheme achieves a significant performance gain by greatly reducing the certificate transmission delay by 50% for both TLS 1.3 and TLS 1.2.

The extensive popularity of Android operating system hones the increased malware attacks and threatens the Android ecosystem. Machine learning is one of the versatile tools to detect legacy and new malware with high accuracy. However, these Machine Learning (ML) models are vulnerable to adversarial attacks, which severely threaten their cybersecurity deployment. To combat the deterrence of ML models against adversarial attacks, we propose a novel randomization method as a defense for image-based detection systems. In addition to defensive randomization, the paper also introduces a novel method, called AutoE, for transforming an APK to an image by leveraging API calls only. To evaluate the effectiveness of randomization as a defense against adversarial settings, we compare our AutoE with two state-of-the-art image-based Android malware detection systems. The experimental results reveal that the randomization is a strong defensive hood for image-based Android malware detection systems against adversarial attacks. Moreover, our novel AutoE detects malware with 96% accuracy and the randomization approach makes it harder against adversarial attacks.

This paper introduces a novel real-time intrusion detection approach for UAV networks, addressing current security challenges. We leverage Generative Adversarial Networks (GANs) to generate synthetic data and implement a stream-based active learning process for prompt detection and response. Our method merges GANs with active learning, involving human experts to navigate the dynamic nature of UAV data and improve detection accuracy. Our results show that this system outperforms traditional intrusion detection methods, promising a new direction for future security frameworks and their application across various networked systems.

In Body Sensor Networks (BSNs), connected nodes on the body exhibit different channel characteristics than any other remote node, such as an attacker who wishes to disrupt the network. Based on this observation, several channel-based Physical Layer Authentication (PLA) protocols have used Received Signal Strength Indicator (RSSI) for authentication. Supported by experiments performed with ESP32 WiFi modules, we introduce a robust feature set that consists of statistical characteristics of RSSI and Frame Loss Rate (FLR) to design a lightweight authentication scheme. To create a realistic dataset in a BSN (using the IEEE 802.15.6 standard), Castalia was used with a network configuration of 6 sensor nodes, a controller node, and an attacker node. To learn the channel behavior of sensor nodes, we adopt a machine learning approach. The model has been trained with a dataset that includes the RSSI and FLR features of 6 sensors and 1 attacker and is capable of classifying received packets by sender, which allows us to detect any attacker trying to send data to the network. The accuracy of the proposed scheme is 95%. Moreover, true attack detection and false attack detection rate is 88%, and 4.1% respectively.

Transport Layer Security (TLS) is widely used for user authentication and encrypted data transmission in all kinds of networks. In its newly published version, TLS 1.3, a 0- RTT handshake protocol is proposed for session resumptions in low delay networks, which makes it possible to secure the data transmission and protect users from being monitored in wireless Internet of Things (IoTs). However, the 0-RTT TLS handshake protocol is vulnerable to the replay attack. In this paper, we propose a Time-Based One-Time Password (TOTP) empowered TLS encryption algorithm to resist replay attacks during the handshake process, in which we propose to integrate the TOTP into the encryption process of the EarlyData. It can significantly improve the forward secrecy of the 0-RTT handshake protocol and its capacity to resist the replay attack. On the other hand, we make no changes to the interaction process of the standardized 0- RTT handshake protocol to guarantee the compatibility of our proposed scheme, which makes our proposed scheme suitable for large area wireless IoTs. Simulation results show that under the premise of choosing an appropriate TOTP update rate, our proposed scheme can effectively resist replay attacks while ensuring the processing efficiency of the system.

Drones are gaining high popularity for their beneficial use in civilian applications and smart cities. Capable of being structured in networks, they can be used to collect several types of data, such as images, and be sent to centers for further processing. At the same time, they also become a new target for multiple types of attacks, among them False Data Injection (FDI), Denial of Service, GPS Spoofing, etc. Therefore, designing new systems and defense mechanisms against these attacks becomes urgent and necessary. In this paper, we emphasize the dangerous nature of the so-called False Data Injection (FDI) and describe a method based on deep learning for its detection. Considered a severe and powerful attack, an injection of false data into the data (images) collected by the drones can considerably alter a final decision that the processing center may take. To fight against this attack, our proposal relies on image analysis and classification using a deep learning approach. After scaling the received image to fit the classifier, using nearest neighbor interpolation (NNI), we feed a convolutional neural network (CNN) to perform image classification. At the end, we compare each class of classification results to a neighborhood using the Mahalanobis distance. Numerical results obtained on the existing dataset [1] demonstrate that our proposal performs well, regardless of the image size, showing an accuracy of 99.21%, a precision of 99.12%, a recall of 99.05%, and an F-score of 0.992%.

With the growing use of Unmanned Aerial Vehicles (UAVs) in military and civilian applications, cyber-attacks are increasing significantly. Therefore, detection of attacks becomes indispensable for such systems. In this paper, we focus on the detection of False Data Injection (FDI) attacks in Unmanned Aerial Systems (UASs). Considered to be the most performed attack, an attacker injects fake data into the system in order to disrupt the final decision. To combat this threat, our proposal is built on image analysis and classification. First, we resize the received image in order to adapt it to feed the classifier using the Nearest Neighbor Interpolation (NNI). Second, we train, validate, and test a Convolutional Neural Network (CNN) to perform the image classification. Finally, we compare each classification result classes to a neighborhood using Euclidean distance. Numerical results on the VisDrone dataset demonstrate the efficiency of our proposal under a set of metrics.

This article proposes an optimal pricing scheme for provisioning sensors-as-a-service (Se-aaS) for catering to applications with multi-tenancy requirements in a sensor-cloud platform. The scheme orchestrates a trade-off analysis between communication range and price in a sensor-cloud platform with range-reconfigurable nodes. The proposed scheme consists of two phases – (a) selection of a neighbor node of a source node, and determination of optimal price for the selected neighbor node. In the first phase, a source node adjusts its communication range and selects its best possible neighbor node usingselectivity factorof all the neighbor nodes. The selectivity factor considers the determinants such as effective residual energy, effective power consumption, and the number of applications to which the neighbor nodes are associated in the neighbor selection process. In the second phase, we design a utility function to determine the optimal price of the selected neighbor node. We use theLagrangianfunction to model the proposed problem as a mixed-integer linear program (MILP) and obtain the optimal solution using the Karush-Kuhn-Tucker (KKT) conditions. The existing works on pricing in sensor-cloud are deficient in considering the presence of the reconfigurable communication range of sensor nodes. Moreover, based on the value of the communication range, the charged price of the sensor nodes varies. Thus, in this article, we propose a pricing scheme with a trade-off of the reconfigurable communication range of sensor nodes and the charged price incurred in adjusting the communication range. Extensive experimental results show that the proposed scheme performs better compared to the existing pricing schemes for sensor-cloud. In precise, the proposed scheme is capable of increasing the average number of neighbor nodes by at least 1.38 percent. Further, the proposed scheme is capable of reducing the charged price by 10.55 percent, as compared to the existing pricing scheme, DOPH.

5G communications are expected to expand both capacity and flexibility in future vehicular networks. However, due to the wide coverage range of 5G-based networks, massive device access in the 5G era will pose great challenges in access control and terminal management. In order to address the scalability issue in large-scale 5G-based vehicular networks, we propose in this paper the use of two heuristic sharding schemes which are based on the Determinantal Point Process (DPP) with different complexities. Specifically, in the proposed algorithms, both location and wireless channel condition of a base station (BS) are jointly considered respectively as diversity and quality parameters in the DPP. Both of them can effectively control the size of each shard, ensure the shards are evenly distributed and allow in-shard cooperation among the BSs. The communication robustness is then greatly improved due to the efficient in-shard cooperation and the system guarantees stable throughput even in scenarios where transactions volume changes dynamically. While compared to benchmark schemes, the simulation results of the proposed protocol and algorithms show significant performance gains in terms of coverage and load balancing.

A Bayesian game theoretic model is developed to dynamically select channel sensing intervals in a massively dense network of Internet of Things. In such networks, the core objective is to minimize every node's energy consumption while having incomplete information about other nodes actively communicating in the network. Selecting channel sensing intervals in a medium access control (MAC) protocol is absolutely crucial, especially in massively dense networks, and selecting intelligently these intervals can optimize the overall network energy consumption while also minimizing latency during the information transfer. In the proposed model, a sensing interval chosen by a node is dynamically derived using current and previous incoming traffic patterns at other nodes in the vicinity. This paper shows that formulating the problem of channel sensing intervals as a Bayesian game model can extensively improve the performance of a MAC protocol when incorporating information from other nodes within the network.

The ubiquity of sensor technology and the Internet of Things prompted us to propose to develop a real-time digital dashboard to visualize the anxiety risks of populations during a pandemic, as in the case of COVID-19. To this end, here we provide an end-to-end communication architecture to detect physiological data related to heart rate, blood pressure, and SPO2, using wearable sensors and communicate them to remote servers. Based on this collected data, the centralized dashboard will classify in real time the patients of each geographic region involved according to a specific attribute, i.e., normal, mild, moderate, high, severe, or extreme. In addition, we also propose to incorporate the emerging technologies of Space Time Frequency Spreading (STFS) and Space-Time Spreading-Aided Indexed Modulation (STS-IM) for the design of the communication links. It has been found that the integration of STFS and STS-IM promises to reduce the likelihood of data disruption for the proposed architecture.

The exhilarating pace of smartphone innovation and vast proliferation to everyday life also impose serious security threats. The open-source and largest android market is also the hive for malware authors. Since the last decade, machine learning (ML) has gained much attraction and successfully deployed and offers unsurpassed versatility for automated malware detection. Unfortunately, as ML-based approaches become widely adopted and deployed, adversaries are also in a never ending race to evade these classifiers for bypassing android malware detection systems. To combat adversarial attacks and secure machine learning-based android malware classifiers, we present a novel image based android malware classifier that has proven its robustness under various adversarial settings. In this work, we have crafted two novel attacks and reveal that the state of the art Android malware detectors are vulnerable and got easily evaded with more than 50% evasion rate. However, the proposed approach establishes a durable defense line against these adversarial attacks and too arduous to bypass.

COVID-19 affects the mental health of many people around the world. In particular, isolation situations due to lockdown have become more challenging for elderly people as they have limited access to technology. At the same time, technologies of remote systems using Internet of Things (IoT) have emerged as a pivotal role for healthcare management and therefore could assist the elderly in managing and improving their mental health and quality of life. In this paper, we suggest the use of wearable devices with health sensors, therapeutic music playback devices, and a cloud-based data collection system as an integrated Internet of Things architecture capable of assessing the level of anxiety and depression of elderly people. Through an accurate monitoring and reporting of the measured temperature, pulse rate, and SpO2, the system is capable of assessing the level of anxiety and depression and triggers the playback of therapeutic music to reduce the level of stress and anxiety. The implementation of the system, using NodeMCU platform, and space time spreading (STS)-aided communication links, emerges as a promising solution to help the healthcare sector and families to manage and reduce the anxiety and depression risks among elderly population.

Today, various mobile applications and wearable devices support the management of diabetes by offering early and remote monitoring facilities. However, most of the available products recommend the activity/exercise level for patients based on standard data about the impact of exercise on calories burnt and blood Glucose levels. There is a risk associated with such products due to lack of customization to the individual patients. In this paper, we propose to use an Internet of Medical Things (IoMT) architecture to predict the level of activity required each day by the patient to maintain the recommended level of blood Glucose. We compare the performance of Artificial Neural Network (ANN) and Support Vector Machine (SVM) for their prediction accuracy. The proposed model takes pre-exercise Glucose level as input parameter and recommends the duration and intensity of the physical activity required by the patient each day. ANN has been observed to perform better for its classification accuracy.

The use of Unmanned Aerial Vehicles (UAVs) in military and civil applications is increased in recent years. These UAVs are generally equipped with a set of sensors and follow a predefined trajectory. The regions of deployment are usually hostile environments and inaccessible such as disaster zone or military fields. Therefore, to accomplish their mission, the UAVs need to communicate with each other, with the Ground Control Station (GCS) and with the navigation satellite system. Through these infrastructures, the UAVs are prone and vulnerable to various cyber-attacks such as GPS spoofing. This attack can be launched easily by an attacker using a simple transmitter by broadcasting fake and wrong GPS signals. These signals can mislead UAVs that receive them from their initial trajectory. In order to ensure the predefined UAV's mission, detecting GPS spoofing attacks is a real challenge and permits to get a high level of flight security, high reliability, and schedule maintenance in time. In this research, we model this issue using a Bayesian network in order to detect this type of attack, and then, the model analyzes and detects the fake GPS signal data. The preliminary results that our proposal provides are promising in terms of a set of metrics.

Advances in wireless networks and the rapid development of electronic components have actively contributed to the emergence of new communication and surveillance systems known as Unmanned Aerial Systems (UASs). In such systems, unmanned aerial vehicles (UAVs) can be used as a wireless ad hoc network and thus provide a communications infrastructure for diverse military or civil applications. For more efficiency, a swarm of drones can be deployed in an area of interest (e.g. disaster areas, battlefields) by forming a flying ad hoc network (FANET) capable of communicating wirelessly with a ground control station (GCS) in a more secure manner. In this work, we particularly focus on the detection of False Data Injection (FDI) attacks in Unmanned Aerial Systems. We propose a new approach based on mobile agents to collect data and an artificial neural network model to identify injected false data. Our approach is validated using realistic datasets, provided by the University of Minnesota UAS Laboratories, and our results show that our proposal outperforms the compared approach by demonstrating higher detection rates (>94%) and lower false positive rates (<; 2.2%).

With the proliferation of handheld devices and due to numerous routes for malware creation, the detection of new sophisticated malware becomes a real challenge. In contrary to conventional machine learning approaches, which require feature engineering and source code analysis, we propose here to use a new RGB-based imaging technique for android malware detection and classification. To combat malware threats, our system is built on a static analysis of the android application packaging (APK) file. First, we perform a novel transformation of the APK file into a lightweight RGB image using a predefined dictionary and intelligent mapping. Second, we train a convolutional neural network on the obtained images for the purpose of signature detection and malware family classification. The experimental results on the AndroZoo [1] dataset show that our system can classify both legacy and new malware applications with a high accuracy of 99.37%, a False Negative Rate (FNR) of 0.8%, and a False Positive Rate (FPR) of 0.39%.

A non-predicted and transient malfunctioning of one or multiple unmanned aerial vehicles (UAVs) is something that may happen over a course of their deployment. Therefore, it is very important to have means to detect these events and take actions for ensuring a high level of reliability, security, and safety of the flight for the predefined mission. In this research, we propose algorithms aiming at the detection and isolation of any faulty UAV so that the performance of the UAVs application is kept at its highest level. To this end, we propose the use of Kullback-Leiler Divergence (KLD) and Artificial Neural Network (ANN) to build algorithms that detect and isolate any faulty UAV. The proposed methods are declined in these two directions: (1) we compute a difference between the internal and external data, use KLD to compute dissimilarities, and detect the UAV that transmits erroneous measurements. (2) Then, we identify the faulty UAV using an ANN model to classify the sensed data using the internal sensed data. The proposed approaches are validated using a real dataset, provided by the Air Lab Failure and Anomaly (ALFA) for UAV fault detection research, and show promising performance.

With the proliferation of Mobile Internet, handheld devices are facing continuous threats from apps that contain malicious intents. These malicious apps, or malware, have the capability of dynamically changing their intended code as they spread. Moreover, the diversity and volume of their variants severely undermine the effectiveness of traditional defenses, which typically use signature-based techniques, and make them unable to detect the previously unknown malware. However, the variants of malware families share typical behavioral patterns reflecting their origin and purpose. The behavioral patterns, obtained either statically or dynamically, can be exploited to detect and classify unknown malware into their known families using machine learning techniques. In this paper, we propose a new approach for detecting and analyzing a malware. Mainly focused on android apps, our approach adopts the two following steps: (1) performs a transformation of an APK file into a lightweight RGB image using a predefined dictionary and intelligent mapping, and (2) trains a convolutional neural network on the obtained images for the purpose of signature detection and malware family classification. The results obtained using the Androzoo dataset show that our system classifies both legacy and new malware apps with high accuracy, low false-negative rate (FNR), and low false-positive rate (FPR).

A smart home is generally equipped with a set of sensors/devices able to provide intelligent and personalized services to end users. These sensors/devices can sense multiple information related to the physical environment and the residents. This information is then transmitted to a central station for further processing through wireless communication. However, the wireless medium is considered vulnerable and the sensors can fail in providing correct measurements. Moreover, a smart home system should also be able to implement a cleaning system of its sensed data and discard those instances that are erroneous or incoherent. To achieve the data quality improvements, this paper proposes a new approach that uses an Artificial Neural Network (ANN) to detect faulty measurements. The proposed scheme can prematurely and efficiently detect outlier data before forwarding it to a central station. The performance of the solution is validated through simulations, using realistic datasets, and compared with other well-known models. Our findings demonstrate that the proposed approach outperforms the compared models in terms of accuracy, f-score, recall and precision metrics.

In wireless sensor networks, an outlier detection algorithm removes from sensed data any possible error, redundancy and malicious injection of fake data. Therefore, such algorithms improve considerably the reliability and the accuracy of the collected data and also reduce the overall energy consumption in the context of large-scale and dense networks. Yet, the proposed algorithms generate huge communication costs due to information exchanges among neighbors and cannot detect outliers for more than one data type. In this paper, we propose a new outlier detection algorithm that is capable of detecting outliers even in the presence of multiple types of data and that does not require any information about the neighborhood. The proposed approach relies on a set of classifiers implemented in each node of a wireless sensor network. The sensed data is therefore classified in either outlier or normal data in a distributed manner. The extensive simulations of the proposed algorithm confirm its outperforming characteristics in terms of detection accuracy, false alarm rate and energy consumption.

Information-centric networking (ICN), a new networking paradigm in which the focal point is a named data, has been proposed recently as an evolving concept to the actual host-centric model of the Internet that relies mainly on host addresses. In vehicular networks, where vehicles are generally moving network elements and follow a content-oriented fashion, it will be fitting to use the ICN paradigm to improve the content dissemination and reduce the content retrieval latency. By applying this concept to such networks, we focus in this paper on the content delivery issue and propose an optimized caching scheme that proactively predicts the moving direction of a vehicle and brings into the next encountered RSU cache only the required content of interest to that vehicle. According to the obtained results from different measured metrics, the proposed solution outperforms in many ways other proposed schemes in the literature. For instance, our scheme improves drastically the cache utilization, enhances the network delay, and boosts the content diversity and distribution.

The Software Defined Networks (SDN) paradigm, often referred to as a radical new idea in networking, promises to dramatically simplify network management by enabling innovation through network programmability. However, notable security issues, such as app-to-control threats, remain a significant concern that impedes SDN from being widely adopted. To cope with those app-to-control threats, this paper proposes a solution to securely deploy valid network applications while protecting the SDN controller against the injection of the malicious application. This problem is mitigated by proposing a novel SDN architecture, dubbed SENAD, which splits the well-known SDN controller into: (1) a data plane controller (DPC), and (2) an application plane controller (APC), to secure this latter by design. The role of the DPC is dedicated for interpreting the network rules into OpenFlow entries and maintaining the communication with the data plane. The role of the APC, however, is to provide a secured runtime for deploying the network applications, including authentication, access control, resource isolation, control, and monitoring applications. We show that this approach can easily shield against any deny of service, caused for instance by the resource exhaustion attack or the malicious command injection, that is caused by the co-existence of a malicious application on the controller's runtime. The evaluation of our architecture shows that the packet_in messages take less than 5 ms to be delivered from the data plane to the application plane on the long range.

Software-Defined Networking (SDN), as a new network paradigm, has the advantages of centralizing control and global visibility over the whole network. However, security issues remain to be a significant concern and impede SDN from being widely adopted. The most straightforward approach to mitigate the threat from malicious OpenFlow applications (OF app) is using permission set for controlling access from OF app to SDN controller. Unfortunately, most of them, if not all, adopt simply static permission control. In this paper, we will address the app-to-control threats along with the four permission categories: READ, ADD, UPDATE and REMOVE on four open source SDN controllers, including OpenDaylight, ONOS, Floodlight, and Ryu. We found that malicious OF app still can infect SDN controllers which are even hardened by the static permission control. Therefore, we present Controller DAC (SDN Controller Dynamic Access Control System), which is a controller-independent dynamic access control system for protecting SDN controllers against API abuse. In our implementation, Controller DAC requires low deployment complexity for securing SDN controllers, and most of time its operation is independent from underlying SDN controller. The preliminary experimental results show that Controller DAC can prevent SDN controllers from API abuse with less than 0.5% performance overhead.

Electrocardiogram (ECG) datasets are among the most challenging records that have been widely studied for early automatic prediction of cardiac anomalies. In order to achieve high performance automatic prediction, existing works make use of complex and time consuming techniques and/or show high rates of false positives. In this paper, we introduce a new method to analyze an ECG dataset and perform an efficient prediction of 7 ST-segment and T-wave anomalies related to Myocardial Infarction (MI) or Ischemia. Our method combines both Decision Trees Boosting and Random Under Sampling (RUS) techniques to respectively improve the prediction performance and solve the class imbalance problem. This method, named RUSBoost, has been validated using data of 7 leads, collected from a real ECG dataset [1], and the obtained results show a higher balance between true and false positives for all the 7 leads. Obtained average sensitivity and specificity are respectively 86% and 94.85%, which outperform the existing results of other related works.

Software-defined networking (SDN), as a new network paradigm, has the advantage of centralizing control and global visibility over a network. However, security issues remain a major concern and prevent SDN from being widely adopted. One of the challenges is the prevention of malicious OpenFlow application (OF app) access to the SDN controller as it opens a programmable northbound interface for third party applications. In this paper, we address app-to-control security issues with focus on five main attack vectors: unauthorized access, illegal function calling, malicious rules injection, resources exhausting and manin-the-middle attack. Based on the identified threat models, we develop a light-weight plug-in, which is called ControllerSEPA, by using RESTful API to defend SDN controller against malicious OF apps. Specifically, ControllerSEPA can provide the services including OF app-based AAA control (unlike OpenDaylight and ONOS which offer user-based or role-based AAA control), rule conflict resolution, OF app isolation, fine-grained access control and encryption. Furthermore, we study the feasibility of deploying ControllerSEPA on five open source SDN controllers: OpenDaylight, ONOS, Floodlight, Ryu and POX. Results show that the deployment operates with very low complexity, and most of time the modification of source codes is unnecessary. In our implementations, the repacked services in ControllerSEPA create negligible latency (0.1% to 0.3%) and can provide more rich services to OF apps.

Abstract Session Initiation Protocol (SIP) is widely used as a signaling protocol to support voice and video communication in addition to other multimedia applications. However, it is vulnerable to several types of attacks because of its open nature and lack of a clear defense line against the increasing spectrum of security threats. Among these threats, flooding attack, known by its destructive impact, targets both of SIP User Agent Server (UAS) and User Agent Client (UAC), leading to a denial of service in Voice over IP applications. In particular, INVITE message is considered as one of the major root causes of flooding attacks in SIP. This is due to the fact that an attacker may send numerous INVITE requests without waiting for responses from the UAS or the proxy in order to exhaust their respective resources. Most of the devised solutions to cope with the flooding attack are either difficult to deploy in practice or require significant changes in the SIP servers implementation. Apart from these challenges, flooding attacks are much more diverse in nature, which makes the task of defeating them a real challenge. In this survey, we present a comprehensive study of flooding attack against SIP, by addressing its different variants and analyzing its consequences. We also classify the existing solutions according to the different flooding behaviors they are dealing with, their types, and targets. Moreover, we conduct a thorough investigation of the main strengths and weaknesses of these solutions and deeply analyze the underlying assumptions of each of them for better understanding of their limitations. Finally, we provide some recommendations for enhancing the effectiveness of the surveyed solutions and address some open challenges. Copyright © 2015 John Wiley & Sons, Ltd.

Abstract In mobile ad hoc networks (MANETs), the nodes need to cooperate each other to establish multi‐hop routes for out‐of‐range wireless communication. However, some of them may not always behave normally, either behaving selfishly for saving computational resource or maliciously for compromising communication protocols. Regardless of intents, such misbehavior would lead to the degradation of network performance. It is therefore important to design appropriate mechanisms to ensure that network performance could be maintained at an acceptable level in the presence of misbehaving nodes. But the open nature of MANETs makes such designs challenging. Reputation system has been widely recognized as an effective approach, which associates the behavior of nodes with its reputation, which is calculated by specifying and quantifying the observations of interest with respect to predefined performance metrics. More interestingly, the observations can be obtained and integrated from multiple layers, facilitating cross‐layer analysis. This paper intends to take a deep look into several well‐studied reputation systems and examine their operational characteristics in terms of modeling approaches and redemption techniques, with an objective to identify their capabilities in terms of misbehavior detection coverage and blind spots. Furthermore, such an anatomy allows us to better understand the failure curses of the deployment and operation of reputation systems in MANETs, so as to improve their performance by adopting effective countermeasures.Copyright © 2014 John Wiley & Sons, Ltd.

Cardiovascular diseases (CVD) are known to be the most widespread causes to death. Therefore, detecting earlier signs of cardiac anomalies is of prominent importance to ease the treatment of any cardiac complication or take appropriate actions. Electrocardiogram (ECG) is used by doctors as an important diagnosis tool and in most cases, it's recorded and analyzed at hospital after the appearance of first symptoms or recorded by patients using a device named holter ECG and analyzed afterward by doctors. In fact, there is a lack of systems able to capture ECG and analyze it remotely before the onset of severe symptoms. With the development of wearable sensor devices having wireless transmission capabilities, there is a need to develop real time systems able to accurately analyze ECG and detect cardiac abnormalities. In this paper, we propose a new CVD detection system using Wireless Body Area Networks (WBAN) technology. This system processes the captured ECG using filtering and Undecimated Wavelet Transform (UWT) techniques to remove noises and extract nine main ECG diagnosis parameters, then the system uses a Bayesian Network Classifier model to classify ECG based on its parameters into four different classes: Normal, Premature Atrial Contraction (PAC), Premature Ventricular Contraction (PVC) and Myocardial Infarction (MI). The experimental results on ECGs from real patients databases show that the average detection rate (TPR) is 96.1% for an average false alarm rate (FPR) of 1.3%.

Cardiovascular diseases are the leading cause of death in the world, and Myocardial Infarction (MI) is the most serious one among those diseases. Patient monitoring for an early detection of MI is important to alert medical assistance and increase the vital prognostic of patients. With the development of wearable sensor devices having wireless transmission capabilities, there is a need to develop real-time applications that are able to accurately detect MI non-invasively. In this paper, we propose a new approach for early detection of MI using wireless body area networks. The proposed approach analyzes the patient electrocardiogram (ECG) in real time and extracts from each ECG cycle the ST elevation which is a significant indicator of an upcoming MI. We use the sequential change point detection algorithm CUmulative SUM (CUSUM) to early detect any deviation in ST elevation time series, and to raise an alarm for healthcare professionals. The experimental results on the ECG of real patients show that our proposed approach can detect MI with low delay and high accuracy.

ABSTRACT Duty cycling is a fundamental approach used in contention‐based medium access control (MAC) protocols for wireless sensor networks (WSNs) to reduce power consumption in sensor nodes. Existing duty cycle‐based MAC protocols use either scheduling or low‐power listening (LPL) to reduce unnecessary energy lost caused by idle listening and overhearing. This paper presents a new asynchronous duty‐cycled MAC protocol for WSN. It introduces a novel dual preamble sampling (DPS) approach to efficiently coordinate channel access among nodes. DPS combines LPL with a short‐strobed preamble approach to significantly reduce the idle‐listening issue in existing asynchronous protocols. We provide detailed analysis of the energy consumption by using well‐known energy models and compare our work with B‐MAC and X‐MAC, two most popular asynchronous duty cycle‐based MAC protocols for WSNs. We also present experimental results based on NS‐2 simulations. We show that depending on the traffic load and preamble length, the proposed MAC protocol improves energy consumption significantly without degrading network performances in terms of delivery ratio and latency. For example, for a traffic rate of 0.1 packets/s and a preamble length of 0.1 s, the average improvement in energy consumption is about 154%. Copyright © 2011 John Wiley & Sons, Ltd.

Anomaly detection in high speed networks is well known to be a challenging problem. It requires generally the analysis of a huge amount of data with high accuracy and low complexity. In this paper, we propose an anomaly detection mechanism against flooding attacks in high speed networks. The proposed mechanism is based on Jensen-Shannon divergence metric over sketch data structure. This sketch is used to reduce the required memory, while monitoring the traffic, by maintaining them into a predefined fixed size of hash tables. This sketch is also used to develop a probabilistic model. The Jensen-Shannon divergence is used for detecting deviations between previously established and current distributions of network traffic. We have implemented our approach and evaluated it using real Internet traffic traces, obtained from MAWI trans-Pacific wide transit link between USA and Japan. Our results show that the proposed approach is scalable and efficient in detecting anomalies without maintaining per-flow state information.

Voice over IP (VoIP) technology has proven to be an immensely useful technology during the last decade. Not only it provides various services to users (e.g. calls, instant messaging, internet conferencing etc), but it is also in a process of extending services (e.g. fax service). Due to the open environment of packet switched network, the signaling protocols (such as SIP and H.323) and the transport protocols (e.g. RTP and SRTP) used by VoIP, it is prone to unwanted accesses and does not comply with the security standards. VoIP is also vulnerable to threats from TCP/IP and UDP. Furthermore, the malicious users can exhaust proxy and end users with INIVTE flooding in SIP. In order to overcome these challenging issues, we propose a simple and robust method to detect INVITE flooding on the SIP proxy server by a strategic proxy model that is based on a user-specified threshold. This model is based on the traffic pattern of normal VoIP traffic and the attacked traffic pattern. Unlike considering only stateful proxies, our model is works for both stateless and stateful proxies.

Abstract While the problem of greedy behavior at the MAC layer has been widely explored in the context of wireless local area networks (WLAN), its study for multi‐hop wireless networks still almost an unexplored and unexplained problem. Indeed, in a wireless local area network, an access point mostly forwards packets sent by wireless nodes over the wired link. In this case, a greedy node can easily get more bandwidth share and starve all other associated contending nodes by manipulating intelligently MAC layer parameters. However, in wireless ad hoc environment, all packets are transmitted in a multi‐hop fashion over wireless links. In this case, an attempting greedy node, if it behaves similarly as in a WLAN, trying to starve all its neighbors, then its next hop forwarder will be also prevented from forwarding its own traffic, which leads obviously to an end to end throughput collapse. In this paper, we show that in order to have a more beneficial greedy behavior in wireless ad hoc network, a node must adopt a different approach than in WLAN to achieve a better performance of its own flows. Then, we present a new strategy to launch such a greedy attack in a proactive routing based wireless ad hoc network. A detailed description of the proposed strategy is provided along with its validation through extensive simulations. The obtained results show that a greedy node, applying the defined strategy, can gain more bandwidth than its neighbors and keep the end‐to‐end throughput of its own flows highly reasonable. Copyright © 2010 John Wiley & Sons, Ltd.

Welcome to this special issue of the Wiley's Wireless Communications and Mobile Computing Journal

Clustering allows efficient data routing and multi-hop communication among the nodes. In this paper, we propose Overlapping Clusters Algorithm (OCA) for mobile ad hoc networks. The goal of OCA is to achieve network reliability and load balancing. The algorithm consists of two discrete phases. The start-up phase takes battery and bandwidth capacity, transmission range, density, mobility, and buffer occupancy as input parameters to performs initial clustering for the entire network. The maintenance phase monitors the status of the network and keeps the network topology updated through local and global re-clustering. We compare the performance of OCA with Lowest ID, Highest Degree, WCA, and LCC algorithms in YAES simulator. The simulation results show that OCA outperforms all the compared algorithms in terms of network reliability and load distribution. The average numbers of global re-clusterings and reaffiliations were much lower in OCA than the other algorithms. However, OCA generates a larger number of clusters, which is expected considering that the nodes are allowed to be members of multiple clusters at the same time.

Wireless mesh Networks (WMNs) are a prominent paradigm of wireless communication that have been widely used in many applications. The growing popularity of such networks opened the door to a profusion of attacks that may target their core functioning leading to a harmful impact on their performance. Hence, the need of robust and fast detection of those attacks became a major prerequisite in order to guarantee an efficient and fair share of network resources among nodes. One of the well known devastating attacks is MAC layer misbehavior which may lead to severe collapse of network performance. In this study, we focus on such misbehavior and in particular on the adaptive greedy behavior of a node in wireless mesh network environment. In such environment, wireless nodes compete to gain access to the medium in order to communicate with a mesh router (MR). In this case, a greedy node may violate the MAC protocol rules to earn extra bandwidth share upon its neighbors. To evade from detection, the cheater node may use more than one technique and switch dynamically between each of them. To counter such misuse, we propose to extend our previous solution, dubbed FLSAC, through the use of a Bayesian statistical model. This new scheme is implemented in conjunction with FLSAC at the mesh router/gateway to monitor the behavior of the attached wireless mesh clients and detect any deviation from the proper protocol rules. The simulation results reveal that this new solution outperforms both of DOMINO and FLSAC in terms of detection rate and accuracy.

The CSMA/CA protocol is the most commonly used medium access mechanism in wireless networks. This protocol schedules properly the access to the medium among all the competing nodes. However, in a hostile environment, such as Mobile Ad Hoc Networks (MANETs), selfish or greedy nodes may prefer to decline the proper use of MAC protocol rules in order to increase their throughput at the expense of their honest neighbors. In this work, we propose a new backoff scheme that allows the neighbors of any node to detect its attempt to deliberately disobey the MAC protocol rules, by either fabricating a small backoff value or refusing to increase its contention window after an unsuccessful transmission. Our scheme uses one way function to generate the backoff values and modifies the RTS frame format by piggybacking the DATA packet's CRC value and the transmission attempt. So any cheating attempt will be detected by the receiver node as well as the other neighbors of the cheater, as long as the monitoring conditions are held. Moreover, our scheme is robust against sender-receiver collusion and provides a novel reaction mechanism to punish the detected cheaters. The simulation results, in different topologies, have confirmed the efficiency of this scheme in terms of fairness index and detection rate.

In wireless ad hoc networks, employing the IEEE 802.11 technology, access to the wireless medium is scheduled according to the CSMA/CA protocol. This protocol was designed with the assumption that nodes would follow properly its operation and never deviate from it. However, it is common to have selfish nodes that may choose to disobey this protocol in order to either gain more bandwidth or degrade the network performance. In this paper we analyze and quantify the impact of each misbehaving technique on the network performance using extensive simulations. We emphasize on the propagation of MAC layer misbehavior effects to the higher layers, particularly to the routing layer, in a pure ad hoc environment.

While the problem of greedy behavior at the MAC layer has been widely explored in the context of wireless local area networks, its study for multi-hop wireless networks still almost an unexplored and unexplained problem. Indeed, in a wireless local area network, an access point mostly forwards packets sent by wireless nodes over the wired link. In this case, a greedy node can easily get more bandwidth share and starve all other associated contending nodes by intelligently manipulating the MAC layer parameters. However, in wireless ad hoc environment, all packets are transmitted in a multi-hop fashion over wireless links. Therefore, if a greedy node behaves similarly as in WLAN case, trying to starve its neighbors, then its next hop forwarding node will also be prevented to forward its own traffic, which leads to an end-to-end throughput collapse. In this paper, we show that in order to have a more beneficial greedy behavior in wireless ad hoc networks, a node must adopt a different approach than in WLAN to achieve a better performance of its own flows. We then present a strategy to launch such greedy attack in a proactive routing based wireless ad hoc network. Through the extensive simulations, the obtained results show that by applying the proposed algorithm, a greedy node can gain more bandwidth than its neighbors and keep the end-to-end throughput of its own flows highly reasonable.

The most commonly used medium access mechanism in WLAN is based on the CSMA/CA protocol. This protocol schedules properly the access to the medium among all competing nodes. However, in a hostile environment, such as wireless local area networks (WLANs), selfish or greedy behaving nodes may prefer to decline the proper use of the protocol's rules in order to increase their bandwidth shares at the expense of well behaving nodes. In this paper, we focus on one such misbehavior and in particular on the adaptive greedy misbehavior of a node in the context of wireless local area network environment. In such environment, wireless nodes compete to gain access to the medium and communicate directly with an access point (AP). In this case, a greedy node may violate the common rules in order to earn extra bandwidth upon its neighbors. In order to avoid its detection, this node may adopt intelligently different techniques and switch dynamically between each of them. To counter such a misbehavior, we propose the use of a fuzzy logic technique in a new detection scheme. This scheme, implemented in the access point, monitors the behavior of associated wireless nodes and reports any deviation from the proper use of the CSMA/CA protocol. The simulation results of the proposed scheme show its robustness and ability to detect and identify quickly most of the deviations of an adaptive cheater.

The CSMA/CA protocol is well known to handle the channel access to various users in wireless ad hoc networks using IEEE 802.11 technology. This protocol requires nodes to wait for some time before initiating a transmission to avoid collisions. As a result, the greedy behavior of some misbehaving nodes can try to lower their waiting time in order to access the channel earlier and penalize the other nodes. In order to avoid this misbehavior, we propose in this paper a model based on measuring the linear regression of nodes' access time to the channel. We have demonstrated that this model exhibits a linear regression between the different nodes' access time. This result has been also confirmed by simulations. In this model, each deviation from the estimated slope is considered as a source of cheating from a corresponding node. By using this detection model, we were able to detect most of the misbehaving nodes in wireless ad hoc networks without requiring modifications to the IEEE 802.11 MAC protocol.

In Mobile Ad Hoc Networks (MANET), anomaly detection and response system (ADRS) plays a paramount role in diagnosing anomalous events, which are resulted by both accidental system errors and intentional attacks. While a variety of ADRS is ready for deployment, there lacks a sound and formal way to examine their operational characteristics for selecting the most appropriate ones with particular concerns. To that end, this paper develops a decision-theoretical framework to identify the fundamental tradeoffs between the key evaluation metrics of ADRS in MANET, along with a formal method to optimize the overall performance of ADRS in terms of those metrics of concern. In particular, each ADRS sensor is treated as an autonomous agent, making its decision as the local operational environment and a global signal that estimates the performance of ADRS as a whole, in terms of detection performance (detection accuracy and false positive rate) and operational cost (detection cost and response cost). The theoretical framework then serves as a basis for developing policy gradient algorithms for practically and automatically inferring the optimal behavior of ADRS sensors. A set of simulations is conducted for validating the feasibility and evaluating the performance of our proposed framework.

As compared to its wired counterpart, wireless network is relatively new and is exposed to some additional threats specific to the underlying medium. Among these threats the jamming attack which can take place easily due to the open nature of wireless medium. A device or person can continuously emit radio signals to disturb a valid conversation. If it lasts for sometime continuously, it can result in total collapse of a network using single channel. In order to evade a jammer in an ad hoc network, we propose a proactive channel hopping scheme based neighbor correspondence. Rather than detect and react we rely on prevention is better than cure. Each node communicates with its neighbors on different channels, coordinated between them dynamically. Furthermore, the control and data channels of each node are separated. This way redundancy at the node-level is provided so that even if nodes on the jammed channel can not be approached but they still are able to contact others by visiting their control channels; avoiding the node on the jammed channel from starvation. Hence, even if the network is exposed to the jammer, a complete failure is prevented. The simulation results show that our scheme is efficient and is able to reduce the jammer's impact significantly, as compared to the scheme presented in [8].

It is well known that security threats, in wireless ad hoc networks, are becoming a serious problem which may lead to harmful consequences on network performance. Despite that, many routing protocols still not resilient to such threats or their countermeasures are not efficient. Moreover, the vulnerability of MAC layer protocols to some attacks exacerbates the damage caused by the threats at higher layers. Therefore, cooperation between layers in compulsory to face such devastating threats. In this paper, we address a cross-layer attack targeting proactive routing protocols, which is launched at the routing level and reinforced at the MAC layer in order to amplify the resulted damage. We demonstrate that this attack can severely compromise the routing protocols and lead to large data packets loss. We particularly analyze it under the Optimized Link State Routing (OLSR) protocol in detail and propose a lightweight solution to cope with it. The simulation results confirm the efficiency of this solution.

Welcome to Zürich, Switzerland and the 2ndInternational IEEE Workshop on Wireless and Internet Services (WISe 2009). The workshop is held in conjunction with the 34thIEEE Conference on Local Computer Networks (LCN 2009), October 20–23, 2009.

Exploiting the Adaptive Coded Modulation (ACM) mechanism leads to a more spectrum-efficiency since the transmission parameters are dynamically adjusted to accommodate the channel conditions. This improvement is, however, limited when used in satellite environments due to the time-varying nature of the channel conditions. This indeed make harder for the transmitter to match instantaneously the current network conditions with the appropriate ACM parameters. The high delays entailed by accurate network condition measurement and end-to-end reporting, and the feedback propagation are a major hurdles to overcome. In this paper we propose to use channel prediction, instead of using the instantaneous channel state feedback, as a mean to combat the counterproductive effects caused by the feedback latency. Based on the predicted values, we propose a new modulation and code rates (MODCOD) selection algorithm, which considers the measurement impairment by introducing some margins allowing the selection of more robust MODCOD. Further, we propose a sliding window technique as a mean to combat the detrimental effects of the frequent MODCOD switching and the entailed oscillations in the system performance. Simulation results elucidate that the proposed scheme allows a more efficient network resources' utilization while maintains the Bit Error Rate (BER) within an acceptable level.

Abstract In several wireless sensor network applications, it is required to perform real‐time reconstruction of the data field being sensed by the network. This task is generally carried out at a central location, e.g. sink node, using a continuous data gathering phase and relying on the known correlation properties of the underlying data field. Estimating the overall spatial and temporal distortion in the reconstructed field is an important step toward deciding the number of sensors to be deployed and the data collection algorithm to be used. However, estimating distortion in arbitrary networks is a challenging task. Existing work has focused on regular network deployments such as one‐ and two‐dimensional girds. Such deployments are deemed infeasible in a realistic environment. In this paper, we consider one‐ and two‐dimensional random networks. For the analysis purposes, we assume that the nodes are randomly deployed following Poisson distribution. We determine the total distortion function given the correlation coefficients of the field while assuming a simple data gathering protocol. Based on this, we also determine the optimal number of nodes to be deployed in the field that will minimize distortion. Copyright © 2009 John Wiley & Sons, Ltd.

While Markov reward models (MRMs) have been widely used for system dependability evaluation, their application for evaluating security still poses as a challenge. It is observed that attacker behavior plays a key role in causing models of security evaluation to be complicated. Another observation is that representing attacker behavior in terms of attack effects instead of attack itself enables the system security to be indirectly evaluated by identifying families of attacks rather than individual instantiations. Furthermore, an attacker behavior tends to be affected by defense mechanisms (we say defender) due to their close interactions. These observations motivate us to boost MRMs to the security context by extracting the behaviors of attacker and defender. To do that, we present a general yet simple state- based approach to characterizing and inferring the behaviors of attackers and defenders in typical network attacks. It specifically contributes in two folds: 1) two objective-oriented models are developed to measure the attacker's and defender's behaviors, respectively; 2) the objectives, actions, and the resultant effects by the attacker and defender, along with the underlying system states, are then integrated and formulated as partially observable Markov decision processes. The developed models and analysis allow the behaviors of attacker and defender to be characterized in a fine-grained way, and specific attack-defense strategies to be inferred approximately via existing model-based algorithms. The system security hereby can be indirectly validated on the basis of the aggregated effects resulted from the interactive behaviors of attacker and defender. A real trace study is conducted to show feasibility and effectiveness of our proposed approach.

It is well known that TCP protocol behaves quiet loosely in mobile wireless environments. In fact, when a mobile host moves from one IP domain to another it has to get a new IP address in the new domain. This process of getting a new IP address should be automatic and fast enough in order to keep active any running TCP session without loosing the quality of service provided to the user. Mobile IP has been developed to manage the user mobility in mobile wireless networks. However, this protocol is not well suited to support a TCP handoffs during the node's handover process which will help in keeping active any running TCP sessions. In this paper, we have designed a new management architecture which will ensure the continuity of any TCP connection when a mobile host executes a handover. In order to validate our architecture, we have evaluated its performance by adopting an analytical model.

A mobile ad hoc network is made of a collection of nodes connected through a wireless medium and form a wireless multihop network with possible changing topologies. The widely accepted existing routing protocols designed to accommodate the needs of such self-organized networks do not address possible threats or attacks aiming at the disruption of the protocol itself. The widely assumed trusted environment is not really the environment that can be realistically expected in reality. In this paper, we describe a new attack against routing protocols which we call virtual link attack, where a misbehaving node tries to relay any Hello message originated from its neighbors aiming to create fake symmetric links in the network. We show that this attack can severely compromise any routing protocol and may lead to large data packets loss. We specifically analyze this attack under the optimized link state routing (OLSR) protocol in detail and devise a symmetric neighbor verification protocol (SNVP) to alleviate its impact and severity.

Exploiting antenna directionality provides significant improvements in terms of spatial reuse, in comparison to omnidirectional antennas, leading to higher network capacity. However, this improvement is highly correlated to the presence of the hidden terminal and deafness problems. In this paper we propose to handle the sensed noise and the arrival of corrupted packets events in a way to address these issues. This is achieved by special settings to the so-called Directional NAV, initially proposed to solve the exposed terminal problem, and the introduction of a special directional CTS control packet, sent to identified deaf nodes, as an invitation to resend a new RTS packet. The simulation results elucidate the effectiveness of the proposed features in addressing both hidden terminal and deafness problems, while increasing significantly the network performance.

In this paper, we address the problem of cooperative black hole attack, one of the major security issues in mobile ad hoc networks. The aim of this attack is to force nodes in the network to choose hostile nodes as relays to disseminate the partial topological information, thereby exploiting the functionality of the routing protocol to retain control packets. In optimized link state routing (OLSR) protocol, if a cooperative black hole attack is launched during the propagation of topology control (TC) packets, the topology information will not be disseminated to the whole network which may lead to routing disruption. In this paper, we investigate the effects of the cooperative black hole attack against OLSR, in which two colluding MPR nodes cooperate in order to disrupt the topology discovery. Then we propose an acknowledgment based technique that overcomes the shortcomings of the OLSR protocol, and makes it less vulnerable to such attacks by identifying and then isolating malicious nodes in the network. The simulation results of the proposed scheme show high detection rate under various scenarios.

Several studies have shown that in high-speed networks it is likely that a large number of packets are dropped in a single loss event on a bottleneck link and a large portion of the ongoing flows are about to experience packet losses. Such synchronized losses among flows have negative effects on the performance of most loss-based congestion control protocols proposed for high-speed networks, especially with respect to fairness considerations. We propose in this paper F2-TCP, a new loss-based congestion control protocol for high-speed networks, to achieve better fairness and TCP-friendliness. Simulation studies show that F2-TCP indeed fulfills these design goals. The superior performance and the low deployment cost, especially on legacy machines, make F2-TCP a good alternative for todaypsilas high-speed networks.

Recently, many QoS architectures have been proposed to handle efficiently multi-service flows in IEEE 802.16 networks. However, these architectures have several weaknesses as they present some scalability issues and donpsilat consider the wireless nature of such networks (e.g. variable link capacity). Moreover, the proposed approaches fail in providing efficient admission control (AC) procedure to tackle congestions at both uplink and downlink channels. In this paper, we introduce new modules in both subscriber station (SS) and base station (BS), allowing more efficient handling of multi-service flows. We particularly focus on the design of a probabilistic and self-configuring AC algorithm, which prevents from uplink and downlink congestions while guaranteeing QoS to rtPS and nrtPS flows. Simulation results show that the proposed admission control protocol highly improves the management of underlying wireless resources, allowing therefore network operators to accept more QoS-enabled services.

Nowadays, we are witnessing an important increase in attacks among which distributed denial-of-service (DDoS) that easily flood the victims using multiple paths. Intrusion detection and filtering are necessary mechanisms to combat against these attacks and secure networks. However, the existing detection techniques for DDoS attacks have their entities work in isolation. In this paper, we propose an efficient and distributed collaborative architecture that allows the placement and the cooperation of the defense entities to better address the main security challenges. The use of content based DHT (distributed hash table) algorithm permits also to improve the scalability and the load balancing of the whole system. This modular architecture has been implemented on IDS (intrusion detection system) entities with the DHT Pastry protocol and has shown a promising performance.

As one of the backup measures of intrusion prevention techniques, intrusion detection system (IDS) plays a paramount role in the second defense line of computer networks. Due to the special infrastructure and communication mode, intrusion detection in wireless mesh networks (WMNs) is especially challenging and requires particular design considerations. In this paper, we propose a novel anomaly detection scheme, called RADAR, to detect anomalous mesh nodes in WMNs. Firstly, we introduce a general concept of reputation to characterize and quantify the mesh node's behavior/status in terms of fine-grained performance metrics. This enables us to construct a robust baseline for leveraging and measuring the derivation between normal and anomalous behavior of each mesh node. Secondly, based on reputation management, we develop a cooperative anomaly detection scheme by fully exploring the spatio-temporal properties of mesh nodes' behavior. Our current scheme is specified and implemented with a reactive routing protocol, aiming at detecting malicious mesh nodes which intentionally violate normal routing mechanisms. The simulation results show that our scheme performs well in terms of detection accuracy, false positive rate, computational overhead, and scalability.

Abstract Vehicularad hocnetworks (VANETs) are becoming very popular and a promising application of the so‐called mobilead hocnetworks (MANET) technology. It has attracted recently an increasing attention from many car manufacturers as well as the wireless communication research community. Despite its tremendous potential to enhance road safety and to facilitate traffic management, VANET suffers from a variety of security and privacy issues which may dramatically limit their applications. In this paper, we address the problem of packet dropping attack launched against routing protocol's control packets, which represents one of the most aggressive attacks in MANET. The aim of this attack is to force nodes in the network to choose hostile nodes as relays to disseminate the partial topological information, thereby exploiting the functionality of the routing protocol to retain control packets. In particular, in optimized link state routing (OLSR) protocol, if a collusive packet dropping attack is launched during the propagation of the topology control (TC) packets, the topology information will fail in being disseminated to the entire network, which finally results in routing disruption. This paper focuses on the packet dropping attack, launched against OLSR, where two malicious multipoint relay (MPR) nodes collude to disrupt the topology discovery process. Based on the analysis of the attacker's behavior and the attack's consequence, we propose an acknowledgement‐based mechanism as a countermeasure to enhance the security of OLSR. This mechanism helps the OLSR protocol to be less vulnerable to such attack by detecting and then isolating malicious nodes in the network. The simulation results of the proposed scheme show high detection rate under various scenarios. Copyright © 2008 John Wiley & Sons, Ltd.

The IEEE 802.11 standard for wireless local area networks (WLANs) employs a mechanism for medium access control (MAC), named distributed coordination function (DCF), which is based on carrier sense multiple access with collision avoidance (CSMA/CA). The collision avoidance mechanism uses the random backoff prior to each frame transmission attempt. The random nature of the backoff reduces the collision probability, but cannot eliminate completely these collisions. It is well known that as the number of contending stations increases, the number of collisions is also likely to increase and the performance of the 802.11 WLAN is significantly compromised. In this paper, we propose a novel distributed MAC protocol, named reduced collision MAC (RC-MAC). In our algorithm, a station access the channel by following a cyclic method. After a certain period of contention resolution, the stations simply organized in a cycle and each of them will access the channel while its turn comes. In this case, there is no more collision in the future and the bandwidth is used efficiently. Through extensive simulations, we show that RC-MAC achieves a significant increase in the overall performance compared to the standard 802.11 DCF.

A particularly severe attack on routing protocols in ad hoc networks is the so-called wormhole attack in which two or more colluding attacking nodes record packets at one location, and tunnel them to another location for a replay at that remote location. When this attack targets specifically routing control packets, the nodes that are close to the attackers are in effect shielded from finding any alternative routes to the remote location with more than one or two hops, and thus all the routes will be directed to the wormhole established by the attackers. In optimized link state routing protocol (OLSR), if a wormhole attack is launched during the propagation of link state packets, the wrong link information will propagate throughout the network, leading to routing disruption. In this paper, we devise an efficient method to detect wormhole attacks in the OLSR protocol. This method tries to ascertain the effective presence of neighbors by employing an efficient neighborhood detection algorithm. Our method has several advantages since it does not require any time synchronization or location information and shows high detection rate under various scenarios.

This paper presents a novel scheme that enables IPv6 mobile networks to perform optimal route optimization. The proposed scheme exploits features of the widely deployed border gateway protocol (BGP). When a mobile network is about to change its point of attachment to the Internet, its mobile router (MR) gets a new care-of-address (CoA) from the visited location and sends a binding update to its home agent (HA). Additionally, MR gets a new temporary network prefix (TNP) at the new location using the prefix delegation protocol. MR then advertises this TNP to its subnet via a router advertisement (RA) message and enables the mobile network nodes (MNNs) to build their own respective CoAs. Simultaneously, this TNP is also sent to the border router (BR) of the home network to enable BR update its BGP routing table. This operation is performed to build an association between the TNP and the mobile network prefix (MNP). BR then notifies its peers of this new update. This procedure will enable any correspondent node (CN) to directly communicate with MNNs, avoiding therefore ingress filtering and reducing both signaling and processing overhead on MR and the home agent (HA). A comparison of the proposed scheme against the NEMO Basic Support scheme, in terms of communication delay, is made via a simple performance analysis.

The efficient use of energy in wireless sensor networks is critical issue as the battery of a sensor node, in most cases, cannot be recharged or replaced after deployment. In order to detect an event, a sensor node spends most of the time in monitoring its environment, during which a significant amount of energy can be saved by placing the radio in the low power sleep mode when no reception and/or transmission of data is involved. In this paper, we discuss the design of a new MAC protocol for wireless sensor networks whose goal is to extend the lifetime of the network by avoiding major energy waste causes, such as collisions, overhearing and idle listening, without compromising other network performance measures such as network throughput. The performance of the protocol is studied by simulation and is compared to that of the well known S-MAC protocol which is designed to save energy and to the IEEE 802.11 protocol which is designed to maximize throughput.

Due to its increasingly growing capacity, WLANs are becoming mature enough to be integrated in a real commercial multi-service offers. However, their success will mainly depend on their ability to provide quality of service for different media types (audio, video, etc.). In this paper, we devise a new protocol which provides a more strict service differentiation between different traffics. The protocol deals also with the well known 802.11 anomaly and improves network throughput by using separate contention window ranges reinforced by a packet length differentiation. Thus, each flow will be seen allocated a selected backoff interval and a packet length from variable bounded ranges to improve both throughput and delay. The different backoffs and packet lengths vary dynamically depending on the propagation conditions. Fairness is then taken into consideration as the adaptation is made sensitive to upper-layers' quality of service metrics as well as to the propagation conditions observed at the PHY layer expressed with SNR value, carrying out a cross- layer architecture.

Energy consumption is a critical issue in wireless sensor networks as the battery of a sensor node, in most cases, cannot be recharged or replaced after deployment. In order to detect an event, a sensor node spends most of the time in monitoring its environment, during which a significant amount of energy can be saved by placing the radio in the low power sleep mode when no reception and/or transmission of data is involved. In this paper, we discuss the design of a new MAC protocol for wireless sensor networks, which mainly avoids overhearing, collisions, and frequent commutation between sleep and active modes. These issues are generally considered to be the most important reasons behind energy waste in heavy loaded conditions of wireless sensor networks. The proposed protocol, called Reservation-MAC (R-MAC), uses two separate periods during the communication process. In the first period, nodes compete for time slots reservation for their future transmissions, and in the second period, each node transmits its data or receive data from a corresponding sender. Once a node is aware of its transmission and/or reception time slot, it stays active only for these time slots and goes back to the sleep mode during the remaining time of the transmission period. In our experiments, the performance of the R-MAC protocol is studied in saturated conditions and compared with the well known S-MAC and T- MAC protocols. Depending on the traffic load, the proposed MAC protocol significantly improves the energy consumption compared to S-MAC and T-MAC.

A particularly severe attack on routing protocols in ad hoc networks is the so-called wormhole attack in which two or more colluding attacking nodes record packets at one location, and tunnel them to another location for a replay at that remote location. When this attack targets specifically routing control packets, the nodes that are close to the attackers are in effect shielded from finding any alternative routes to the remote location with more than one or two hops, and thus all the routes are directed to the wormhole established by the attackers. In optimized link state routing protocol (OLSR), if a wormhole attack is launched during the propagation of link state packets, the wrong link information propagates throughout the network, leading to routing disruption. In this paper, we devise an efficient method to detect and avoid wormhole attacks in the OLSR protocol. This method tries first to infer links that may potentially lead to wormhole tunnels. The proper wormhole detection was then be applied to suspicious links by means of an exchange of encrypted probing packets between the two supposed neighbors (endpoints of the wormhole). Our solution has several advantages since it does not require any time synchronization or location information and shows high detection rate under various scenarios.

This paper describes the architecture of a global sensor network based on a constellation of LEO satellites. The considered sensor network is heterogeneous: two types of sensor nodes are envisioned. One type does the sensing and relays the gathered data to the other type that performs data aggregation and communicates it directly to the satellites. The main challenging tasks in the design of the architecture are explored and adequate solutions are provided. A set of data dissemination techniques is then presented. Following this, a mathematical model is developed to evaluate the energy use of the sensors. Open research issues for the realization of such architecture are finally discussed.

This paper considers the problem of routing in sensor networks from the point of view od data collection. That is, given the initial amount of battery energy in each node, the aim is to determine how much data can each source transmit until the network is partitioned (i. e., until the nodes cannot find end-to-end routes to their respective sinks). In addition, to respond to some specific applications' requirements, when determining such nodal data volume distribution, fairness among nodes is taken into account. The problem is formulated as a concave utility maximization and a sub-gradient algorithm is proposed to solve it distributively. Some numerical results are given and the convergence of the algorithm is discussed.

Sensor networks are deployed to gather some useful data from a field and forward it toward a set of base stations or sinks for data analysis and decision making. Each sensor node is endowed with a finite amount of energy, and each byte transmission or reception costs a certain fixed fraction of energy as well as a variable fraction that depends on the distance between sender and receiver. Maximizing the volume of data collected at the sinks until some particular set of nodes exhaust their battery and partition the network is a very desirable trait in sensor networks, as it equates with a high level of energy efficiency. In this paper we formulate the problem of maximum data collection routing for sensor networks as a utility maximization problem subject to energy constraints, and invoke lagrange relaxation, duality and sub-gradient technique to solve the problem. We then focus on the problem of path oscillation, which is well known to happen in routing algorithms where link costs are function of the traffic load and propose heuristic solutions to address this oscillation problem