With the massive growth of wireless service comes the concern about location privacy. Even with the use of pseudonyms and anonymous communication techniques, the threat of traffic analysis continues to be worrisome. Particularly in the context of Internet of Things (IoT) applications, data packets often flow towards a certain user node or a Base Station (BS), which makes such a recipient a sink in the network and elevates its susceptibility to cyberattacks. Existing countermeasures assume that the attacker will employ one of the contemporary analysis engines, such as Evidence theory and transmission rate, as the underlying data correlation mechanism. This paper presents a novel traffic analysis model that is based on Convolutional Neural Networks (CNN). We demonstrate that such a new attack mechanism is so powerful that it could overcome conventional countermeasures and hence warrants extensive investigation to devise protective measures.

Cyberattacks pose persistent threats to wireless communication, especially in low-power IoT devices where conventional cryptography is impractical. This work leverages Physical Unclonable Functions (PUFs) to deterministically configure a session-specific modulation profile. For each session, the PUF response to a challenge dynamically determines both the modulation order, and also permuting the bit-to-symbol mapping to create a unique, obfuscated QAM constellation. This ensures that each communication instance is uniquely encoded and obfuscated at the physical layer. The receiver uses the same challenge-response pair to infer the session specific mapping and to demodulate the signal. This dynamic constellation generation thwarts adversaries by preventing pattern reuse and rendering modulation recognition by machine learning models ineffective. Importantly, the session-specific and device-dependent nature of the scheme ensures that even if a node is compromised, other nodes remain secure. The approach operates entirely at the signal level and introduces minimal computational burden for IoT and embedded communication platforms.

Remote and accurate keystroke recognition is essential for improving human-computer interaction and developing advanced analysis techniques. We propose a novel system that combines WiFi Channel State Information (CSI), acoustic signal analysis, and large language models (LLMs) to enhance keystroke recognition accuracy. While prior research has demonstrated the effectiveness of WiFi signals for detecting keystrokes by analyzing unique patterns in channel state information (CSI) variations, we extend this approach by incorporating acoustic data from keyboard strokes, as well as the contextual capabilities of LLMs. By combining two modalities (WiFi CSI and acoustic data), we increase the accuracy and robustness of keystroke detection. This dual-input approach combined with leveraging commercially available LLMs, not only detects and identifies keystrokes but also recognizes complete words and sentences. We have been able to enhance accuracy using WiFi CSI augmented with acoustic information to achieve keystroke recognition accuracy of$\mathbf{8 1. 5 \%}$. With the inclusion of LLM in our system, our keystroke identification accuracy for sentences has increased to 99 %.

The widespread adoption of wireless communication technologies has intensified spectrum demand, exacerbating spectrum scarcity despite underutilization in many frequency bands. Cognitive radio systems offer a solution by supporting dynamic spectrum access, particularly through passive spectrum monitoring for opportunistic use. Existing machine learning (ML) and deep learning (DL) based white space detectors fall short of the accuracy needed for large-scale networks. This paper presents the Autoencoder-Transformer Integrated Cognitive Radio (ATIC) framework, a lightweight model that leverages the Transformer's self-attention mechanism to effectively capture complex spectraltemporal patterns without extensive preprocessing. ATIC's robustness is validated on both field-collected 5G Non-Standalone (NSA) and synthetic datasets, achieving an accuracy of 99.77 %. Furthermore, ATIC's architecture supports parallel processing across multiple primary resource blocks (PRBs), optimizing inference time as PRB count increases and achieving a 32% reduction compared to convolutional neural network (CNN).

Interweave Cognitive radio (CR) technologies enables support of infotainment and multimedia services in vehicular ad hoc networks (VANETs), especially during accidents or traffic congestion. The dynamic nature of VANET topology and fluctuating traffic volumes makes machine learning (ML) techniques promising for spectrum sensing; however, centralized training of ML models can lead to significant drawbacks, such as high communication overhead and data privacy concerns due to the transfer of large amounts of raw data. While adaptation of federated learning (FL) can address this problem, on-device training of deep learning with a federated approach poses computational challenges for vehicles. To address these limitations, this paper proposes a novel lightweight spectrum detection framework integrating spiking neural networks (SNNs) and FL. The SNN's event-driven nature reduces computational complexity, while FL enables decentralized training across vehicles, preserving data privacy and minimizing communication overhead by transmitting model updates rather than raw data. Validation using field-collected LTE subsystem reservations of 5G Non-Standalone (NSA) confirms that the proposed approach achieves fast inference times, enhancing spectrum sensing efficiency and expanding spectral resources for vehicular communication.

An Internet of Things (IoT) engages a large number of resource-constrained edge devices that are internetworked over multi-hop paths. Remote attestation refers to checking the integrity of the software onboard the individual devices (provers) to ensure that it is not maliciously tampered with. Given the scale and the lack of direct communication links, attestation often needs to be conducted over a multi-hop path between a verifier and a prover which raises concern about the integrity and confidentiality of the provided response (digest). To address these issues, this paper proposes PAMA a lightweight attestation protocol utilizing Physical Unclonable Functions (PUFs) to obfuscate the digest and sustain its confidentiality. PAMA does not reveal the actual digest and deprive any node on the dissemination path from even replaying an old attestation response. The response of multiple provers is further aggregated to improve efficiency and scalability while enabling the verifier to authenticate the devices on the path and determine which of them has failed the digest validation if any. PAMA's robustness is rigorously analyzed, and its performance is validated through simulation. The results show that PAMA outperforms competing schemes in terms of runtime complexity, bandwidth efficiency and energy consumption.

Internet of Things (IoT) systems comprise a large number of resource-constrained edge devices that collaborate to serve application tasks. Remote attestation refers to checking the integrity of the software onboard the individual devices to ensure that it is not tampered with or infected by malware. Swarm attestation refers to collective integrity check of multiple devices as a means to cope with the large network size. Yet, existing swarm attestation schemes fall short of identifying the suspected devices and/or achieving important security properties. To fill the technical gap, this paper proposes PISA a lightweight attestation and aggregation framework utilizing Physical Unclonable Functions (PUFs) as the Root-of-Trust in both digest generation and aggregation, within a multi-prover network. The objective is to ensure the privacy and integrity of individual device attestation reports, verifying the authenticity of attestation requests across the network while also identifying the devices that fail the verification. PISA employs a network partitioning algorithm and optimized aggregation paths to amortize overhead and support scalability. The security properties of PISA are rigorously analyzed. The validation results show that PISA outperforms competing schemes, offering enhanced runtime and bandwidth efficiency and reduced energy consumption.

With the increasing popularity of small computing devices and applications of IoT, the need for platform integrity grows both in scale and scope. In particular, the detection of successful attempts to inject a malicious software module or modify an existing one is of utmost importance. This paper promotes LiSB, a novel approach for validating software/firmware integrity and ensuring secure boot-up for resource-constrained embedded devices. LiSB is lightweight, yet very robust. A hardware primitive is used as a Root-of-Trust to support the confidentiality of generated digests and the security of the attestation protocol. Specifically, LiSB employs Physically Unclonable Functions (PUFs) to make the digest device-specific without storing any secrets in the device memory. The performance and robustness of LiSB are validated using a prototype implementation on an FPGA. The results demonstrate that LiSB outperforms recently-published and prominent commercial attestation schemes like TPM, and consumes 25 times less power than SHA-256, which serves as the core component of most existing attestation schemes. The security properties of LiSB are formally analyzed.

Periodic spatial-temporal safety messages play a significant role in supporting driving safety in Intelligent Transportation Systems (ITS). These messages include GPS coordination and are communicated between ITS nodes at small intervals to increase situational awareness and alleviate the risk of collision. However, even when pseudonyms are used rather than real IDs, an adversary can exploit the safety messages to deanonymize the trajectories of the ITS nodes. Specifically, the adversary can correlate collected pseudonyms to draw traces for node trajectory. This paper opts to tackle such vulnerability and proposes a novel certificate-less pseudonym scheme that withstands trajectory tracking attacks in ITS. Our scheme enables each node to autonomously generate a single-use pseudonym for every message while allowing a particular pseudonym to be generated by multiple nodes to increase false positive correlation of pseudonyms with nodes. Unlike competing approaches, we study the complete lifecycle of pseudonyms and show that our scheme is secure and lightweight. The simulation results demonstrate that our scheme considerably increases the anonymity of the ITS nodes and achieves a very low traceability rate.

Physically Unclonable Functions (PUFs) are lightweight hardware primitives that provide an effective secure-by-design solution for mutual authentication of the resource-constrained Internet of Things (IoT) devices. By embedding PUFs in the design of each IoT device, the identity of devices could be validated using unique device fingerprints, known as challenge and response pairs (CRPs). Unfortunately, PUF-based authentication protocols are susceptible to CRP disclosure which could be exploited to model the PUF and allow impersonation of the associated device by either an external adversary or compromised nodes. This paper presents a Distributed Unclonable Hardware-assisted Authentication and Key Agreement Protocol (DUHAP). DUHAP presents a novel and effective mitigation for CRP disclosure, PUF modeling, and device impersonation. In particular, the PUF responses are obfuscated using varying salt that is dependent on the verifier and the actual PUF response. Such an identity- and response-based obfuscation technique withstands CRPs disclosure attempts by a compromised node or multiple colluding nodes through reverse engineering. The security of DUHAP is analyzed using a prominent protocol validation tool, and its resiliency against modeling is validated using data collected from a popular PUF cryptanalysis toolbox.

The notion of Cyber-Physical Systems (CPS) reflects real-time control applications that are realized through distributed coordination among multiple modules. Such coordination is founded on frequent exchange of status and sensor data among the various modules so that actuation decisions are made autonomously. The formation of digital twins has emerged as an effective methodology where data-driven models are employed to enable effective decision making. Hence, the accuracy of these models become very critical for system stability; no wonder data forgery is a major threat for CPS where an attacker strives to inject faulty data to degrade the digital twin of one or multiple modules. Such an attack could be taking the form of impersonating a component, or manipulating/replaying status update packets. This paper proposes an effective scheme for mitigating such a threat by employing hardware-based fingerprinting primitives, namely, Physically Unclonable Functions (PUFs). The proposed PUF-based Integrity protection of digital Twins (PIT) scheme, ensures the authenticity of data sources, and the freshness and integrity of the shared status. PIT is validated using analysis and prototype implementation on an FPGA.

In cognitive communication, detecting white space is critical for preventing interference with primary user’s transmissions. Rather than modifying the radio transceivers, several machine learning (ML)-based detection techniques have been proposed, where a model is trained offline and then employed to infer white spaces in real-time. Despite their viability for spectrum sensing, these techniques are computationally complex, especially when deep neural network (DNN) models are pursued. Moreover, training a centralized model requires transmitting massive data to the fusion center (FC), which ultimately results in congestion on the transmission channel rather than flagging opportunities for cognitive transmissions. This paper opts to fill the technical gap by proposing: (1) a federated learning model that enables effective design of spectrum monitors in a distributed manner, and (2) a spiking neural network (SNN)-based lightweight white space detector that solves the issue of computationally expensive DNN models and is suitable for resource-constrained devices. The SNN-based federated learning (SFL) model employs secondary users to train their respective SNN models using local data (spatial locality) and sends the gradient of SNN to FC. FC combines the individual SNN models and sends the aggregated model back to each edge node. Validation using live LTE data has demonstrated the effectiveness of SFL with a detection accuracy of 91.16%.

The Dedicated Short-Range Communication (DSRC) protocol has become the de facto means for supporting vehicle ad hoc networking (VANET). Yet, the current allocated spectrum for DSRC is insufficient for handling large volumes of data, particularly during accidents or high traffic congestion, where most spectral resources are dedicated to control and emergency awareness, degrading infotainment and multimedia application services. Such a challenge motivates the pursuance of cognitive radio (CR), also known as CR-VANET, where vehicles opportunistically tap to utilize resources within the licensed spectrum as secondary users (SUs). To avoid interference with primary users (PUs), a vehicle needs to accurately sense the medium and detect white space. Given the dynamic nature of the VANET topology and varying volume of message traffic, machine-learning (ML) techniques, particularly those pursuing deep learning models, proved to be a viable option for spectrum sensing. However, the computational complexity of these techniques becomes an obstacle for vehicles. This paper opts to fill the technical gap by proposing a novel lightweight spectrum detector that employs spiking neural networks (SNN). The validation results using a field collected LTE-dataset clearly show that our proposed SNN model enables fast inference time, which ultimately expedites the spectrum sensing process and provides additional spectral resources for vehicular communication.

An Internet of Mobile Things (IoMT) refers to an internetworked group of pervasive devices that coordinate their motion and task execution through frequent status and data exchange. An IoMT could be serving critical applications such as military reconnaissance, security surveillance, etc., and hence the authenticity, integrity and confidentiality of the transmitted data must be ensured. Yet, achieving the security goals is challenging due to the dynamic nature of the network topology, the constrained computational and communication resources, and the variety of packet traffic patterns among the nodes. This paper proposes an effective solution that leverages lightweight hardware primitives, specifically, Physical Unclonable Functions (PUFs), to support secure communication in the network. The employed PUFs are used to generate peer-to-peer encryption keys to protect the data traffic among nodes and to the command center, while coping with the dynamic change of the network topology. Our solution efficiently supports both unicast and multicast communications. The proposed solution is validated through analysis and prototype implementation on an FPGA.

A Cyber-Physical System (CPS) employs intercon-nected sensing and actuation modules and applies distributed control strategies. With the major advances in communication technology, the CPS design methodology is getting broadly adopted, including in safety and mission-critical applications. The incorporation of digital twins within a CPS facilitates localized decision-making by the individual control modules within the system in a timely manner without risking stability and performance. However, cyberattacks could be detrimental when false data is injected to degrade the accuracy of the underlying digital twins so that a CPS module takes non-optimal or even risky action that causes application failure. This paper proposes a novel approach for detecting such an attack scenario through a combination of a predictive data model and a topology fingerprinting scheme. Specifically, we employ a recurrent neural network (RNN) to predict the next state (data) for the individual modules and use it to reason about the periodic updates provided by these modules. Then, we apply a data-driven fingerprinting scheme that characterizes the inter-module interaction to infer and classify anomalies based on the module-provided data. The validation results using a dataset of a smart power grid application demonstrate the effectiveness of our approach.

Optoacoustic communication enables an airborne unit to directly reach nodes deep underwater. To achieve high data rates in optoacoustic communications, implementing a multilevel modulation scheme is necessary where distinct acoustic signals can convey multiple symbols. However, demodulating these signals proves challenging amidst the complexities of underwater environments characterized by multipath propagation and resultant inter-symbol interferences. To overcome these challenges, this paper presents a novel demodulation scheme using a Spiking Neural Network (SNN). Our SNN model has undergone training using a laboratory-constructed dataset, comprising eight levels of optoacoustic signals recorded from three different underwater positions. Validation is conducted with a dataset deliberately designed to include severe interference from multipath-generated echoes and reverberations. The results indicate that our SNN-based demodulation scheme achieves an impressive accuracy of 90.16%, surpassing the 65.30% accuracy obtained through conventional peak detection-based techniques.

The major advances in wireless communication technology have led to increased adoption across almost all application domains. However, the massive growth has caused spectrum scarcity despite the fact that many of the frequency bands are not fully-utilized. Cognitive radios have emerged as a viable means to support dynamic spectrum access. Particularly, supporting opportunistic access through passive spectrum monitoring is of great interest. Existing techniques for detecting white space either require modification to commodity radio transceivers, or involve computationally complex models that do not suit resource-constrained devices. This paper opts to fill the technical gap by proposing a novel lightweight white space detector that employs spiking neural networks (SNN). SNN is a bio-inspired technique for creating data-driven models. The proposed design relies on the sensed energy in the medium to determine whether a primary user is active. The validation results using live LTE data demonstrate the effectiveness of our novel detector. Suitability for edge devices is confirmed through implementation on a Raspberry-PI platform.

In many applications of underwater networks, surface nodes cannot be employed for security and logistical reasons. For these scenarios an airborne unit ought to reach the underwater nodes directly from the air in order to provide commands to and configure the network. In particular, the airborne unit should facilitate localizing the underwater nodes and enable the establishment of a global coordinate system. Visible Light Communication (VLC) stands out as a prime choice for communication across the air-water interface. Using VLC, an underwater node uses the light intensity to infer proximity of the beam incident point. When receiving a sufficient number of VLC transmissions, prior work could localize the underwater nodes. However, such an approach requires very fine-grained area coverage, especially in shallow water environments, which is not practical in many application scenarios. This paper tackles such a limitation by introducing a novel Hybrid Airborne-Enabled Underwater Localization (HAUL) method. HAUL leverages knowledge of the underwater network topology in the localization process. Specifically, both acoustic ranging and light intensity measurements are used to estimate the node's global coordinates. HAUL is validated through extensive MATLAB simulations, demonstrating its effectiveness in calculating accurate positions.

Transitioning to Electric Vehicles (EVs) is quite beneficial to the environment. However, the mass adoption of EVs is significantly hindered by battery-related challenges, such as restricted driving range, long charging time, and insufficient charging infrastructure. EV-to-EV charging has emerged as a promising means for addressing the aforementioned concerns. The energy supplier (ES) can replenish the batteries of the energy requesters (ER) while the vehicles are on the move. In this paper, we define the problem of routing and matching EVs over a time-space network and develop a dynamic programming approach to effectively find the solution. The primary goal of this study is to optimize the supplier's profitability within a certain timeframe, considering factors such as battery degradation and overhead. The simulation results confirm the effectiveness of our proposed approach in terms of maximizing supplier's profit and minimizing the energy consumption, travel time, and distance for requesters.

Intelligent Transportation Systems (ITS) opt to improve safety and efficiency by internetworking vehicles, road infrastructure, pedestrians, etc. Given the ad-hoc connectivity and dynamic topology of such a network, robust authentication of member nodes is essential. The authentication process should also suit the resource constrained ITS nodes. This paper proposes an efficient approach for Distributed Authentication for ITS (DAITS). DAITS employs drivers’ mobile devices to act as verifiers, and hence message authentication is provided in an as-a-service basis for the ITS nodes. Moreover, DAITS is a certificateless system, which deploys private smart contracts in a permissioned blockchain, for certifying nodes. Furthermore, the smart contracts store authentication tokens for the ITS nodes which ensure authentication between the ITS nodes and road infrastructure. DAITS relies on lightweight security primitives such as hash function, bitwise XOR, and Hash-based Message Authentication Code (HMAC). Extensive security analysis shows that DAITS can resist various security attacks. The simulation results demonstrate that DAITS is both resource-efficient and scalable, and outperforms competing schemes in terms of computation and communication overhead, and verification delay.

Internet of Vehicles (IoV) is a distributed network supporting communication in real time between vehicles, pedestrians, and roadside infrastructure. An utmost challenge in such an integrated network is how to ensure the authenticity of the communicating vehicles and protect the network against impersonation, message replay and Sybil attacks. Compared to traditional authentication mechanisms, a major concern in IoV authentication is how to achieve real time verification of vehicle identities without violating the user's location privacy. The approach should also be lizhtweight to cope with the constrained computational resources of the vehicle. This paper proposes a privacy-preserving protocol that not only provides the vehicle user's anonymous authentication but also resists Sybil attacks through multiple registrations. Our protocol leverages the usage of lightweight hardware flngerprinting primitives and the properties of elliptic curves. Particularly we employ physically unclonable functions (PUFs). An elliptic curve serves as an implicit dynamic one-way transformation of the hardware fingerprint to ensure the anonymity of the sources and provide further forward secrecy. The robustness of the proposed approach is validated using data collected from an FPGA-based PUF implementation.

Node authentication is essential in IoT systems where interconnected resource-constrained devices operate autonomously. Developing a lightweight authentication technique is requisite to prevent malicious nodes from joining the network and impersonating legitimate nodes. This paper develops a novel protocol to enable mutual authentication for node pairs. The protocol combines the advantage of hardware-based security primitives, namely physically unclonable functions (PUFs), and agility and configurability of physical-layer communication mechanisms, specifically the Multi-Input Multi-Output (MIMO) method. Distinct from existing PUF-based techniques, our protocol allows two devices to mutually authenticate each other without the involvement of an intermediary server. Moreover, the proposed protocol encodes the transmitted information to protect it from any possible eavesdropping attempts that aim to model the PUF of a legitimate node. The experimental results demonstrate the efficacy of the proposed protocol against modeling attacks and impersonation attempts.

As infrastructure becomes smarter and filled with sensors, the need to combine device functionality is more apparent. Reducing the number of devices by introducing multifunctional sensors will reduce cost and overall complexity. There has been a rapid increase in the development of wireless sensing using WiFi Channel State Information (CSI). WiFi sensing using COTS devices has been a valuable tool in supporting multiple applications such as object movement tracking. One unconventional utility of CSI is to sense meteorological conditions. Temperature, humidity and air pressure are important factors for many applications from air traffic safety to personal health. Given the broad deployment of WiFi, the use of the involved devices in assessing meteorological conditions would enable better coverage and mitigate the cost of employing specialized sensors. This paper empirically demonstrates WiFi sensing, where variations in the CSI are used to estimate humidity using machine learning.

Monitoring distributed service-based cloud applications and understanding the interactions among the different components are crucial to diagnose and resolve performance issues. However, many existing cloud monitoring systems require sophisticated application and/or platform instrumentation and cannot be deployed on-demand, or they provide only partial functionality. In most cases, monitoring comes with a significant overhead. To overcome these shortcomings, this paper presents DyMonD, a holistic framework that Dynamically Monitors an application, Discovers the service components, and visualizes them together with some performance metrics such as throughput in the form of a call graph. DyMonD is completely decoupled from the internals of the applications and the services themselves, as it deploys monitoring agents transparently at the software switches within the network, extracts all necessary information from the messages exchanged by the components, and performs service identification by using deep learning on the network flows. Our evaluation shows that DyMonD has significantly less overhead than existing tools, reducing the monitoring-induced impact on response time by up to 89%, and reducing resource consumption such as CPU and memory usage by up to 75%. Furthermore, DyMond’s deep learning module identifies services up to 11% more accurately than competing models.

Workflow scheduling is the process of optimally assigning virtual machines to workflow tasks subject to response time and cost consideration. Since such an optimization problem is NP-compete, providing an effective heuristic approach is essential. In this article, we consider the workflow scheduling problem with the least cost subject to a bound on the response time. We show that existing solutions fundamentally care for the longest execution path within the workflow without appropriately handling non-critical paths. To overcome such a shortcoming, we propose a novel heuristic algorithm based on discrete mathematics. We first demonstrate that a workflow has a bijective relation with a partially ordered set and then introduce two operations on the workflow to show that it is an algebraic structure. We then form a totally ordered set,$(T^{exe}({\mathcal P}), \preccurlyeq)$, of workflow paths where$T^{exe}({\mathcal P})$is the set of path execution times. Based on$(T^{exe}({\mathcal P}), \preccurlyeq),$we identify the path with the maximum cumulative execution time and allocate a virtual machine to each task of the path based on the workflow deadline. We then delete the scheduled path and run our algorithm for each resulting sub-workflow in parallel. The results indicate that the proposed algorithm outperforms the best-known approaches in the literature.

Contactless charging is a promising technology that is gaining increased attention nowadays. Such technology enables Electric Vehicles (EVs) to wirelessly replenish their energy supply while moving over special charging pads. To cope with the high energy demand, a large number of charging pads are placed over a long distance to fulfill EV energy requests. While being an attractive alternative to conventional charging stations, dynamic contactless charging introduces major technical challenges due to the large number of vehicles being served and the high traveling speed of EVs. Specifically, it is necessary for the resource-constrained charging pads to ensure that only authorized EVs are served and to maintain a robust and transparent billing system. To tackle these challenges, we present a lightweight EV authentication protocol that enables a charging-pad to rapidly confirm the identity of an EV and validate the requested transaction. Our proposed protocol employs a PUF and LFSR-based stream cipher and enables a charging pad to authenticate an EV in just less than 0.051 ms. We further integrate the protocol with a secure and transparent energy trading system that is based on blockchain technology. Our protocol has been verified using a popular verification tool and is shown to be secure.

Pervasive sensing is shaping up modern societies and opening the door for many unconventional applications. Instead of the contemporary access model where sensor data is disseminated to a single user, multi-access scenarios are becoming more prevalent, which raises the issue of how to authenticate users, how to ensure access authorization, and how to prevent information leakage. To address these issues, this paper presents a novel lightweight protocol that promotes a data-driven methodology. The idea is to employ hardware primitives to support authentication of legit data recipients and to factor in the previously shared data samples in generating encryption keys. Our protocol in essence generates encryption keys that vary per packet and in an implicitly synchronized manner between the data source and each recipient. The generated key is also a function of the hardware primitive and thus effectively prevents data access to unauthorized recipients. We analyze the resilience of our protocol to impersonation and message replay, and hardware primitive modeling attacks. The security properties of our solution is validated using the AVISPA toolset and its performance is compared to the asymmetric cryptography approaches.

In this work, we present a learning algorithm for a wireless communications network to transmit decoy packets to counter an adversarial sensing-reactive jammer. As the jammer is required to search across channels for data transmissions, decoy packets can have the effect of stalling the jammer on a particular channel, preventing it from continuing its search and leaving legitimate packets unimpeded. A reinforcement learning algorithm trains a deep neural network with an exploration-exploitation algorithm and experience replay. The state- and action-space and reward function are presented as components of the reinforcement learning framework. Our algorithm is tested with software simulations, modeling ZigBee communications nodes using time-division multiple access for medium access control. A reactive jammer is modeled in the simulation, with the goal of disrupting any detected ZigBee transmissions. A means to measure and distribute the reward function and system state to enable edge-learning in this context is presented as part of the implementation. The results demonstrate the effectiveness of our algorithm in mitigating the jamming attack, outperforming a random decoy strategy by a factor of two.

The scale, unattended-operation and ad-hoc nature of an Internet-of-Things (IoT) make the network vulnerable to device impersonation, message replay, and Sybil attacks by either external actors or compromised nodes. This paper opts to tackle such vulnerability and presents a novel and effective solution for mutual authentication of IoT nodes. The proposed solution calls for embedding a Physically Unclonable Function (PUF) on each device, and employs a lightweight protocol for validating the identity of the individual devices based on querying the PUF. To authenticate a “prover” node, a verifier node will send a challenge bit-stream to the prover, where the latter provides the response of its PUF to such a challenge to be matched by what the verifier expects. To prevent the PUF of a prover from being modeled by an eavesdropper or a collusive set of compromised verifiers, the proposed protocol makes the response to a challenge dependent on the verifier. In addition, our protocol combines such an identity-based response generation with a simple Elliptic curve to thwart any attempts by a compromised verifier to reverse engineer the response generation process. The robustness of our PUF-based IoT Device Authentication (PIDA) protocol, is validated using data collected from an FPGA-based implementation.

The optoacoustic process can solve the longstanding challenge of wireless information transmission from an airborne unit to an underwater node (UWN). The nonlinear optoacoustic signal generated by proper laser parameters can propagate long distances in water. However, forming such a signal requires a high-power laser, and the buildup of a vapor cloud precludes the subsequent acoustic signal generation. Therefore, pursuing the traditional on-off keying (OOK) modulation technique will limit the data rate and power efficiency. In this paper, we analyze different modulation techniques and propose a vapor cloud delayed-differential pulse position modulation (VCD-DPPM) technique to improve the data rate and achieve high power efficiency for a single stationary laser transmitter. The symbol rate of VCD-DPPM is approximately 6.9 times and 1.69 times higher than OOK in our text communication simulation using a laser repetition rate of 10 kHz and 40 Hz, respectively. Furthermore, VCD-DPPM is 137% more power efficient than the OOK technique for both cases. We have generated different acoustic signal levels in laboratory conditions and simulated the bit error rate (BER) for different depths and positions of the UWN, while considering ambient underwater noises. Our results indicate that VCD-DPPM enables efficient data transmission.

Motivated by the quest for decreased healthcare costs and further fueled by the COVID pandemic, wearable devices have gained major attention in recent years. Yet, their secure usage and patients’ privacy continue to be concerning. To address these issues, the paper presents SWeeT, a novel lightweight protocol for allowing flexible and secure access to the collected data by multiple caregivers while sustaining the patient’s privacy. Particularly, SWeeT deploys Physically Unclonabale Functions (PUFs) to generate encryption keys to safeguard the patients’ data during transmission. The computation overhead is significantly reduced by applying very simple encryption operations while enabling frequent change of the keys to sustain robustness. SWeeT is shown to counter impersonation, Sybil, man-in-the-middle, and forgery attacks. SweeT is validated through experiments using implementation on an Artix7 FPGA and through formal security analysis.

The Internet of Things refers to the large-scale internetworking of diverse devices, many of them with very limited computational resources. Given the ad-hoc formation of the network and dynamic membership of nodes, device authentication is critical to prevent malicious devices from joining the network and impersonating legitimate nodes. The paper develops a novel authentication mechanism that is based on the incorporation of a physical unclonable function (PUF) in each device. Unlike existing PUF-based schemes, our mechanism enables mutual authentication of two devices without the involvement of a trusted third party. The proposed mechanism also obfuscates the shared information to safeguard it from eavesdroppers who strive to model the underlying PUF using machine learning techniques. The validation results confirm the resiliency of our mechanism against impersonation, message replay and PUF modeling attacks.

Localization of underwater networks is important in many military and civil applications. Because GPS receivers do not work below the water surface, traditional localization methods form a relative topology of underwater nodes (UWNs) and utilize either anchor nodes or floating gateways with dual transceivers in order to determine global coordinates. However, these methods introduce logistical complications and security risks in deploying the anchor and/or surface gateways. This paper tackles such an issue by proposing new localization techniques which can remotely localize UWNs using optoacoustic signals. In our approach, GPS coordinates are transmitted from air to the UWN via creating an underwater temporary isotropic acoustic transmitter with the optoacoustic process. We analyze the process of controlling the shape and size of the plasma to create the isotropic acoustic transmitter and experimentally validate the generation of isotropic acoustic signals. Then two methods of localization are proposed for static and dynamic UWNs. Finally, the simulation results with experimental values show the effectiveness of our approach. Comparing to the traditional techniques, our approach achieves the same accuracy without using any surface or underwater anchor nodes.

Federated learning (FL) is a collaborative framework that aggregates multiple machine learning (ML) models and thus enables efficient handling of data collected from numerous and diverse IoT devices. However, traditional FL frameworks often do not consider the connectivity and the heterogeneity of the data sources and hence suffer accuracy imbalance among the individual (local) ML models and in turn, negatively impact the aggregated model. To mitigate such a shortcoming, this paper promotes a two-step optimization process. The first focuses on data offloading from devices to fog nodes with the objective of improving the accuracy of local ML models under resource and connectivity constraints. The second provides statistical distribution aware data offloading that trades off the communication cost and the accuracy of local ML models. We validate the advantages of our approach using a benchmark dataset.

The popular means for safeguarding against cyberattacks is to employ an intrusion detection system (IDS). Contemporary IDS designs apply machine learning (ML)-based approaches to recognize attack signatures. Yet, the dynamic nature of an Edge-of-Things (EoT) requires continual IDS adaptation by incorporating new intelligence and gained knowledge from security logs in order to detect unknown malicious behaviors. The scale of the system makes the collection of voluminous logs to be impractical. Moreover, sharing security logs by the involved devices would raise privacy concerns. This paper overcomes these challenges by proposing a novel IDS for EoT. The proposed IDS employs federated learning to enable edge nodes to share a model rather than raw data and aggregate the provided models in a hierarchical manner. In addition, our approach recognizes the presence of any individual or colluding attempts to degrade the IDS by providing erroneous (poisonous) data. We apply an iterative voting algorithm to associate trust to participating devices and a Louvain method for uncovering collusive communities. The validation results using a public dataset confirm the effectiveness of our approach.

Physical unclonable functions (PUFs) offer a promising solution for the authentication of Internet of Things (IoT) devices as they provide unique fingerprints for the underlying devices through their challenge–response pairs. However, PUFs have been shown to be vulnerable to modeling attacks. In this article, we propose a novel protocol to thwart such vulnerability by limiting the adversary’s ability to intercept the whole challenge bits exchanged with IoT nodes. We split the challenge bits over multiple messages and engage one or multiple helper nodes in the dissemination process. We further study the implications of various parts of the challenge patterns on the modeling attack and propose extensions of our protocol that exploit bits scrambling and padding to ameliorate the attack resiliency. The experimental results extracted from a 16-bit and a 64-bit arbiter-PUF implemented on FPGA demonstrate the effectiveness of the proposed methods in boosting the robustness of IoT authentication.

Hardware fingerprinting has emerged as a viable option for safeguarding IoT devices from cyberattacks. Such a fingerprint is used to not only authenticate the interconnected devices but also to derive cryptographic keys for ensuring data integrity and confidentiality. A Physically Unclonable Function (PUF) is deemed as an effective fingerprinting mechanism for resource-constrained IoT devices since it is simple to implement and imposes little overhead. A PUF design is realized based on the unintentional variations of microelectronics manufacturing processes. When queried with input bits (challenge), a PUF outputs a response that depends on such variations and this uniquely identifies the device. However, machine learning techniques constitute a threat where intercepted challenge-response pairs (CRPs) could be used to model the PUF and predict its output. This paper proposes an adversarial machine learning based methodology to counter such a threat. An effective label flipping approach is proposed where the attacker's model is poisoned by providing wrong CRPs. We employ an adaptive poisoning strategy that factors in potentially leaked information, i.e., the intercepted CRPs, and introduces randomness in the poisoning pattern to prevent exclusion of these wrong CRPs as outliers. The server and client use a lightweight procedure to coordinate and predict poisoned CRP exchanges. Specifically, we employ the same pseudo random number generator at communicating parties to ensure synchronization and consensus between them, and to vary the poisoning pattern over time. Our approach has been validated using datasets generated via a PUF implementation on an FPGA. The results have confirmed the effectiveness of our approach in defeating prominent PUF modeling attack techniques in the literature.

Underwater acoustic networks operate in an inhomogeneous and dynamic environment, which makes it difficult to model the propagation path of signals. In essence acoustic signals experience reflection and refraction due to sound speed variation, based on many parameters such as salinity, temperature, and depth. To enable modeling of signal propagation, the sound speed profile (SSP) has to be accurately estimated. The most famous SSP equation has been proposed by Mackenzie and has been widely used among others like the Coppens’ and UNESCO equations. The drawback of these equations is that they yield different accuracy levels for various setups. They are also valid only for certain limits of salinity, depth and temperature. Moreover, the SSP estimation method should suit both deep and shallow water environments. In this paper, we use machine learning algorithms to predict sound speed in both deep and shallow waters and compare our results with data collected from acoustic tomography measurements. For training we have considered sound speed measurements across various oceans like Pacific Ocean, Arctic Ocean, Indian Ocean, etc. Our results show that our model achieves 99.99% accuracy and outperforms Leroy and Mackenzie equations.

Dynamic charging is a promising technology for Electric Vehicles (EVs) since it allows EVs to replenish its energy supply while on the move. The popular technology for such dynamic recharging utilizes magnetic induction by placing a large number of special charging pads on the roads that EVs pass over while traveling. Unlike the traditional stationary systems, dynamic charging introduces several challenges in how to handle billing, conduct EV authentication, and sustain privacy. The main issue is attributed to the high motion speed of EVs which allows a very short contact time between the resource constrained charging pads and the EVs. Therefore, we propose a lightweight and fast authentication protocol for EV-to-charging-pads; the protocol is incorporated in an energy trading scheme for the dynamic charging of EVs that is based on blockchain technology. We utilize Physically Unclonable Function (PUF) in the creation of a charging ticket in order to prevent double-spending of the ticket without incurring additional overhead. Furthermore, we leverage pseudonyms to preserve the privacy of EVs. Our analysis demonstrates that the proposed protocol is secure and allows a charging pad to authenticate EV in less than 13 µsec.

In this paper, a deep learning framework for distributed interference assessment and channel selection in a congested, uncooperative spectrum is proposed. We present a deep learning based spectrum sensing model that is trained to estimate channel quality. Such training is conducted offline in a supervised manner using labeled channel energy captures and their associated packet error rate measurements. Packet error rate estimates are derived from numerical simulations of the system model. These estimates can be used to take action on network channel selection. Distributed application of our model is efficiently realized through online training at the level of edge devices and then the trained local models are periodically aggregated using federated learning. The model views packet error rate assessment as a classification problem, where the packet error rate is assigned a class based on a set of classes with upper and lower bounds. The trained models are tested against two metrics: classification accuracy and the mean ratio of error rate in the optimal operating channel to that in a channel selected based on classification inference. In the simulation scenarios, the model when trained in a centralized manner has a classification accuracy of 91.9% and a mean selection error rate ratio 91.3%, averaged over all scenarios. When trained in a distributed manner, the model has a classification accuracy of 83.3% and a mean selection error rate ratio of 88.6%. The distributed approach is estimated to reduce the communications overhead by two orders of magnitude to approximately 34 MB per edge device.

The edge computing paradigm has been adopted in many Internet-of-Things (IoT) applications to improve responsiveness and conserve communication resources. However, such high agility and efficiency come with increased cyber threats. Intrusion detection systems (IDS) have been the primary means for guarding networked computing assets against hacking attempts. The popular design methodology for IDS relies on the application of machine learning (ML) techniques that use intelligence data to classify malicious activities. However, in the realm of IoT, insufficient data is available to build IDS; hence a distributed intrusion system with continual data collection is primordial to refine the detection model. Such IDS is also subject to privacy constraints and should sustain robustness against data manipulation from internal attackers that degrade the ML model. This paper opts to fulfill these requirements by proposing a novel distributed IDS for IoT. The proposed system employs federated learning to enable privacy preservation and diminish the communication overhead. Our system promotes a reinforcement mechanism to ensure resiliency to data manipulation attacks by single or colluding internal actors. The validation results using recently released datasets demonstrate the effectiveness of our approach.

Localization of underwater networks has received lots of attention. However, existing scheme focuses on establishing a relative topology where a node's position is defined in relation to one another. Provisioning global coordinates is achieved only through the inclusion of a surface node that can serve as a reference. This paper opts to tackle the global localization problem in setting where surface nodes cannot be deployed or should be avoided. Our approach is to establish visible light communication (VLC) links across the air-water interface. An airborne unit is to transmit its GPS position using VLC. Upon receiving such a message, the underwater node will factor in the light intensity and coverage to determine its own position relative to the airborne node and consequently its own GPS coordinates. The simulation results demonstrate the effectiveness of our approach and high positioning accuracy.

A Cyber-Physical System (CPS) refers to the interconnection of control (actuation), computational nodes and sensors, in order to manage physical processes. In recent years, the CPS design methodology has been adopted in several large-scale infrastructures such as smart power grids. Given the application criticality, sustaining the security of these systems is of utmost importance. One of the major security goals is to protect CPS against impersonation, where an adversary intends to manipulate the system state by sending erroneous data that appears to be reported by one of the system nodes, e.g. PMUs of a power grid. This paper proposes a novel hardware-assisted authentication scheme to counter such a threat, by exploiting imperfections that occur in the manufacturing process of integrated circuits. In essence, the proposed scheme associates a fingerprint for each system node so that the authenticity of the data source could be verified. In addition, the paper tackles the threat of message replay where the adversary re-transmits a legitimate message so that the system factors in outdated rather than fresh sensor measurements. This paper thwarts such a replay attack by leveraging the synchronized clocks across the CPS nodes, e.g., based on GPS; the idea is to employ a combination of time-stamp signatures and hardware fingerprints. Our proposed schemes can also detect and prevent data forgery, and Sybil attacks. The viability and performance of the proposed schemes are validated through analysis and prototype implementation.

In underwater wireless networks, optoacoustic energy conversion using high energy laser pulse is the only known viable option for communication from an airborne unit to a node at large depth, e.g., a submarine or an unmanned underwater vehicle. However, controlling the generated acoustic signal through this process is very complex. Specifically, if the repetition rate of laser pulses is low, the corresponding acoustic signal is very broadband. The higher frequency components of this broadband signal attenuate more if the underwater node is very far from the surface. Hence, a relatively narrowband signal with lower frequency components is desirable for long distance communication. The frequency component of the broadband acoustic signal depends on the incident angle of the laser light and observation angle of the receiver, i.e., the position of the underwater hydrophone. Both of these angles also change continuously for a wavy water surface, which makes it more complex to determine the frequency components of this kind of signal. In this paper, we show that by carefully choosing the relative position of the airborne unit and underwater node, we can generate a narrowband acoustic signal with lower frequency components for both flat and wavy water surfaces. We provide theoretical analysis and simulation results to capture the effect of these angles on the generated acoustic signals. We further provide guidelines for optimum angle setting for improving the quality of the optoacoustic communication link.

The differential pulse position modulation (DPPM) is one of the popular power-efficient schemes for supporting visible light communication in underwater environments and across the air-water interface. Despite such an advantage, DPPM does not efficiently utilize the available channel capacity. This paper aspires to tackle such shortcomings by striking a better balance between power and bandwidth efficiency. Particularly, L-DPPM is considered where a block of M input data bits is mapped into one of the L distinct waveforms containing only one ‘on’ chip. A novel encoding algorithm and frame structure are proposed in order to shorten the time between consecutive symbols and consequently improve the bit rate of L-DPPM. The idea is based on avoiding bit patterns that contribute the most to bandwidth inefficiency. The proposed algorithm explores a number of bit patterns remapping through simple complement and shifting operations. A detailed frame structure with all necessary control bits is provided. Overall, boosting the bandwidth efficacy comes at the expense of a slight increase in control bit count and transmission power. The simulation results demonstrate the effectiveness of the proposed encoding algorithm and provide guidelines for determining M for best performance.

Automatic identification of the service type used by network flows (e.g., HTTP and MySQL) is an essential part of many cloud management and monitoring tasks for quality of service, security monitoring, resource allocation, etc. Several studies have adapted deep learning models for accurate service type identification of network traffic. These models vary in how the message flow data is used and what datasets are considered. There are no published guidelines on selecting the best approach for automating the service identification process. In this paper, we opt to fill such a technical gap and provide a detailed study of the trade-offs of different deep-learning based approaches for service type identification of network traffic. Towards this end, we generate flow-based datasets for a wide range of service types that are commonly deployed in the cloud. We consider two different deep learning models that have shown promising results in this context, and show their performance for both payload- and header-based datasets, considering fundamental parameters such as dynamic service port configuration, flow direction and the packet order in the flow stream.

Manufacturing variations introduce features that distinguish radio transceivers even those of the same vendor. Such distinction is often referred to as radiometric signature and is found to be useful in conducting device authentication and crime forensics. Yet, radiometric signatures could constitute a privacy threat. Particularly, in the realm of wireless networks, an adversary may exploit RF fingerprinting to identify devices and conduct traffic analysis in order to uncover the topology and categorize the role of various nodes. In this paper, we show that RF fingerprinting could be a major tool for the adversary to distinguish among nodes and bypass the provisioned anonymity protection in the network. We analyze the accuracy of RF fingerprinting and highlight how the accuracy affects the success of adversary attacks. We further develop a novel countermeasure to degrade the adversary's ability in exploiting RF fingerprinting. The proposed countermeasure is based on switching among preset communication protocols and employs adversarial machine learning to select the protocol for a transmission so that the accuracy of the RF fingerprinting diminishes. We demonstrate the effectiveness of our scheme through simulation and prototype experiments.

Nowadays, many cloud applications can be considered large complex distributed services. The increasing sophistication and complexity have made performance monitoring a major issue and a critical process for both cloud providers and cloud customers. Existing monitoring techniques instrument the applications to collect measurements and log them at the host nodes on which the application is deployed. Such an approach introduces overhead and can slow down the application. New paradigms such as Software Defined Networking (SDN) and Network Function Virtualization (NFV) show promise for moving some of the measurement collection and logging functionality into the network as a lot of information can be extracted from messages exchanged between application components. Such a methodology could enable the cloud infrastructure to provide a Monitoring as a Service to applications in a transparent manner without software instrumentation and allowing for a more flexible placement of logging functionality. In this paper, we explore mechanisms to integrate application monitoring into SDN. In particular, we analyze whether switch based message filtering is feasible and we propose a customized port sniffing approach. We discuss the implementation aspects using OVS. The results confirm that moving application monitoring to the network is indeed an attractive option.

In a smart power grid, phasor measurement devices provide critical status updates in order to enable stabilization of the grid against fluctuations in power demands and component failures. Particularly the trend is to employ a large number of phasor measurement units (PMUs) that are inter-networked through wireless links. We tackle the vulnerability of such a wireless PMU network to message replay and false data injection (FDI) attacks. We propose a novel approach for avoiding explicit data transmission through PMU measurements prediction. Our methodology is based on applying advanced machine learning techniques to forecast what values will be reported and associate a level of confidence in such prediction. Instead of sending the actual measurements, the PMU sends the difference between actual and predicted values along with the confidence level. By applying the same technique at the grid control or data aggregation unit, our approach implicitly makes such a unit aware of the actual measurements and enables authentication of the source of the transmission. Our approach is data-driven and varies over time; thus it increases the PMU network resilience against message replay and FDI attempts since the adversary's messages will violate the data prediction protocol. The effectiveness of approach is validated using datasets for the IEEE 14 and IEEE 39 bus systems and through security analysis.

Ride-sharing is a service that enables drivers to share their trips with other riders, contributing to improving traffic congestion as well as assist in reducing Carbon Dioxide (CO2) emission and fuel consumption. It has come to the forefront in recent years as a Green service in large cities. However, the majority of existing ride-sharing services rely on a central third party, which makes them subject to a single point of failure and privacy disclosure concerns by both internal and external attackers. Moreover, they are vulnerable to distributed denial of service (DDoS) and Sybil attacks due to malicious users. There is also high service fees paid to the ride-sharing service provider. In this paper, we propose to decentralize ride-sharing services based on a public Blockchain. Our scheme enables drivers to propose ride-sharing services without relying on a trusted third party. To preserve location privacy, riders send cloaked ride requests to hide their exact pick-up/drop-off locations, and departure/arrival dates. Then, by using an off-line matching technique, drivers sends their offers encrypted to ensure data confidentiality. Upon receiving the ride-offers, the rider can find a ride match using some heuristics as well as the bid price included in the offer. To preserve anonymity, riders/drivers use pseudonyms that change per trip to ensure unlinkabilty. We envision the application of this technology in Green Internet of Things connected smart cities, where ride sharing services are common. Finally, we implement our scheme and deploy it in a test net of Ethereum. The experimental results show the applicability of our protocol.

In this paper, we focus on the problem of offloading of jobs composed of dependent tasks in a mobile edge network (MEN). We formalise the problem and develop a heuristic lower the completion time of a job executing in MEN than when executing on a mobile device. We show the viability of the heuristic through our simulation results.

Underwater wireless networks (UWNs) conventionally have been interfaced to remote control centers through floating nodes that serve as gateways. A gateway will have an acoustic transceiver to communicate with underwater nodes and interact with command nodes over radio airwaves. This paper presents a novel approach that avoids the need for surface gateways and enables effective communication through the air water interface using free space optics. We first analyze the effect of optical transmission angle on the underwater signal range. Based on such analysis we determine the optical beam angle and underwater node position for best signal strength. We further develop a scheme for extending the coverage by beamforming of multiple light sources to mitigate the effect of water current on the receiver position and enable the establishment of robust communication links between underwater nodes and an air-borne base-station.

The availability of high-performance computing platforms, large storage devices, and high- speed communications have boosted the popularity of cloud computing. Users exploit these capabilities by using the cloud as a repository for their data and sharing these data with others. However, since the cloud is usually owned and operated by private companies, storing sensitive data in the cloud servers raises privacy concerns. To address these concerns, privacy-preserving keyword search schemes have been developed. Nevertheless, most of the existing schemes are either inefficient for multi-data- owner settings or designed for single-data-owner settings, and becomes insecure and inefficient when used for multi-data-owner. This paper proposes an efficient multi-keyword ranked search scheme over encrypted data for multi-data-owner settings. The proposed scheme allows each data owner and each user to have a distinct key, and allows the server to efficiently search the files of different data owners using one encrypted query sent by the user. Our privacy analysis demonstrates that the proposed scheme can preserve the privacy of the data owners and users. In addition, our extensive performance evaluations demonstrate that our scheme is much more efficient than existing approaches in the literature.

The performance of wireless body area networks (WBANs) may be degraded due to co-channel interference, i.e., when sensors of different coexisting WBANs transmit at the same time-slots using the same channel. In this paper, we exploit the 16 channels available in the 2.4 GHz unlicensed international band of ZIGBEE, and propose a distributed scheme that opts to avoid interference through channel to time-slot hopping based on Latin rectangles, DAIL. In DAIL, each WBAN's coordinator picks a Latin rectangle whose rows are ZIGBEE channels and colunms are time-slots of its superframe. Subsequently, it assigns a unique symbol to each sensor; this latter forms a transmission pattern according to distinct positions of its symbol in the rectangle, such that collisions among different transnnssions of coexisting WBANs are minimized. We further present an analytical model that derives bounds on the collision probability of each sensor's transmission in the network. In addition, the efficiency of DAIL in interference mitigation has been validated by simulations.

Acoustic Underwater Networks (AUNs) operate in a dynamic and inhomogeneous medium where acoustic signals tend to refract causing difficulty in sustaining connectivity and locating lost nodes. Such inhomogeneity is usually apprehended by observing sound speed variations in aquatic mediums, which is obtained by taking measurements at discrete depths, using historical samples, or by applying ocean acoustic tomography. However, these techniques do not capture the medium dynamicity or pose constraints that are often difficult to achieve in such a habitat. To overcome these shortcomings, we proposes a Distributed Real-time Oceanic Profiling approach (DROP) that relies on few randomly-deployed unsynchronized nodes to estimate underwater sound speed profile (SSP). We first present a method to map the 3D environment into an equivalent 2D one. Using the 2D map, DROP uses a second order degree polynomial to estimate the trajectory of a transmitted signal and exploits the slope gradient of the polynomial in generating a layered medium. DROP then combines the measured local sound speed values and the signal refraction to predict sound speed in distinct layers and obtains an SSP. We validate DROP through simulation and show that its performance is consistent with actual measured data.

Underwater environments are categorized as inhomogeneous habitats which complicates determining long range propagation losses. Basically, transmitted signals tend to refract and change direction in accordance to the encountered sound speed profile (SSP). Therefore, a uniform spreading beam quickly loses its uniformity and complicating the estimation of the signal strength at a beam's wave front. Such complexity degrades a node's ability in determining both the optimum direction and transmitted power to reach neighbors, leading to wasting energy and increases interference. This paper addresses these shortcomings and proposes a propagation loss estimation technique. Our technique introduces a 2D path loss model that leverages both the known SSP and our parabolic ranging method. Then a 3D extension of the model is proposed that utilizes a geographic grid to study the effect of refraction on directional beams. Essentially, the grid is used to identify candidate beams and factor the departure angles and observed SSP to account for medium inhomogeneity. We then utilize the loss models and the wave front shape to determine the best communication angle that minimizes power requirements. We validate our approach through simulation and assess its performance through comparison with contemporary methods.

Wireless sensor networks (WSNs) serving in hostile environments are susceptible to multiple collocated failures due to explosives and natural calamities such as avalanches, landslides, etc., which can partition the network into disjoint segments. In such a scenario, federating the segments becomes essential for reestablishing communication among segments and resuming the WSN operation. Placing relays has been a popular strategy for forming a connected inter-segment topology. In this paper, we tackle the federation problem while considering constrained relay availability. We exploit the use of a limited number of mobile relays to provide intermittent inter-segment connectivity. We propose FLOWER, a novel algorithm for Federation with LOW inter-segment latency and Even Relay load. FLOWER groups the segments into clusters and forms a star inter-cluster topology where each cluster is served by a distinct mobile relay. A cluster at the center of the area serves as a hub to bound inter-segment communication delay. In addition, FLOWER opts to prolong the lifetime of the mobile relays by balancing the energy overhead due to travel and wireless communication. We validate its performance through extensive simulation experiments.

Recent years have witnessed massive growth in the popularity of smart portable devices. These devices are fully powered with a number of network interfaces, such as Wi-Fi, Bluetooth, and 4G, etc., in order to facilitate connectivity and enable the realization of computing and reachability everywhere and all the time. However, in areas with degraded cellular services and lack of Wi-Fi coverage, these devices do not allow connectivity to the Internet. Such a limitation can be particularly detrimental in application scenarios like search and rescue during and after a disastrous event, since trapped individuals cannot be reached. This paper presents a new framework for overcoming such limitation by introducing a hot-spot that may be accessible over device-to-device (D2D) communication links. Specifically, Wi-Fi direct (WFD) is exploited to form multi-hop peer-to-peer (P2P) routes to the introduced hot-spot. WFD supports the same speed and range of the normal Wi-Fi without the need of any infrastructure. WFD is among the emerging network technologies that are becoming almost standard on all new smart devices. By exploiting nearby devices, which have Internet access, our framework will enable users to publish contents. Our framework is validated through implementation using Android.

Recent advances in microelectronics have enabled the realization of Wireless Body Area Networks (WBANs). However, the massive growth in wireless devices and the push for interconnecting these devices to form an Internet of Things (IoT) can be challenging for WBANs; hence robust communication is necessary through careful medium access arbitration. In this paper, we propose a new protocol to enable WBAN operation within an IoT. Basically, we leverage the emerging Bluetooth Low Energy technology (BLE) and promote the integration of a BLE transceiver and a Cognitive Radio module (CR) within the WBAN coordinator. Accordingly, a BLE informs WBANs through announcements about the frequency channels that are being used in their vicinity. To mitigate interference, the superframe's active period is extended to involve not only a Time Division Multiple Access (TDMA) frame, but also a Flexible Channel Selection (FCS) and a Flexible Backup TDMA (FBTDMA) frames. The WBAN sensors that experience interference on the default channel within the TDMA frame will eventually switch to another Interference Mitigation Channel (IMC). With the help of CR, an IMC is selected for a WBAN and each interfering sensor will be allocated a time-slot within the (FBTDMA) frame to retransmit using such IMC.

Localization services are very critical for many applications of wireless networks. The use of the received signal strength (RSS) is quite popular in the localization process. RSS-based methodologies fall into two categories based on whether the RSS measurements are used to estimate distance or simply to infer relative proximity to certain anchors. In this paper, we analytically derive the probability of error in the distance and proximity estimations caused by the non-monotonicity of RSS, under most popular radio propagation models. We further analyze the localization error for both area-based and multilateration algorithms which use the proximity and the distance information, respectively, as a basis to estimate the position. The analytical results show the advantage of using RSS for proximity over distance estimation. The probability of wrong proximity estimation is found to be much smaller than that of distance. The comparison between area-based and multilateration under various settings shows that area-based localization is more robust than multilateration in minimizing location estimation errors particularly when the radio propagation cannot be accurately modeled.

Deploying a networked set of robots is an effective way to serve applications in environments where human intervention is impossible or possess risks. For example, a team of robots can assist rescuers to map, navigate indoor hazardous areas in rescue operation. Collaboration among the robots is very essential in these applications in order to efficiently achieve the aimed goals in a timely manner. Realising such a collaborative operation autonomously in the absence of GPS services is a challenge. This study tackles this challenge assuming sensors/landmarks are present in the deployment area. Each sensor/landmark requires a specific number of robots to perform certain tasks. A spatial–temporal coverage solution is pursued to maintain connectivity and overcome the shortage of available robots. Dynamic coverage problem is formulated as potential fields where landmarks and nodes exert virtual forces among each other based on coverage demand and overlapped area. The proposed approach has been validated through extensive simulation using NS3 simulator and real experimentation using EV3 robots. The proposed approached has shown maximal task satisfaction compared with random waypoint and very close behaviour compared with a centralised approach (Hungarian method).

In applications of Wireless Sensor Networks (WSNs), all data packets are directed towards a single base-station (BS) over multi-hop routes. The BS is usually responsible for processing the collected data and interfacing the WSN to remote users. The continuous flow of packets towards the BS enables the adversary to analyze the traffic and uncover the BS position. In this paper, we present a technique to counter such a threat by creating the illusion that many base stations are deployed into the WSN. The basic idea is to introduce multiple deceptive relay nodes that generate redundant packets such that the traffic pattern of the network is altered to implicate the presence of multiple data sinks. We present an algorithm that forms a load balanced routing tree (LBRT) for each sink. LBRTs are used to determine the deceptive relay nodes, define the deceptive packet generation rate and route the real data. The simulation results confirm that our proposed countermeasure effectively enhances the location privacy of the BS without significant impact on the network's performance and lifetime.

Wireless sensor networks are valuable assets to both the commercial and military communities with applications ranging from industrial control on a factory floor to reconnaissance of a hostile border. In most applications, the sensors act as data sources and forward information generated by event triggers to a central sink or base station (BS). The unique role of the BS makes it a natural target for an adversary that desires to achieve the most impactful attack possible against a WSN with the least amount of effort. Even if a WSN employs conventional security mechanisms such as encryption and authentication, an adversary may apply traffic analysis techniques to identify the BS. This motivates a significant need for improved BS anonymity to protect the identity, role, and location of the BS. Previous work has shown distributed beamforming to be an effective technique to boost BS anonymity in WSNs; however, the energy consumption of these networks depends on helper relay availability and the energy required to recruit relays. In this paper we propose a novel, cross-layer link cost that balances the relay recruitment energy and the number of recruited relays. By incorporating available helper relays directly into the routing link cost, we select routes that maximize the use of distributed beamforming at each hop. We use simulation to demonstrate that our link cost preserves anonymity and reduces energy consumption to levels below those of WSNs that employ no anonymity protection.

When sensors of different coexisting wireless body area networks (WBANs) transmit at the same time using the same channel, a co-channel interference is experienced and hence the performance of the involved WBANs may be degraded. In this paper, we exploit the 16 channels available in the 2.4 GHz international band of ZIGBEE, and propose a distributed scheme that avoids interference through predictable channel hopping based on Latin rectangles, namely, CHIM. In the proposed CHIM scheme, each WBAN's coordinator picks a Latin rectangle whose rows are ZIGBEE channels and columns are sensor IDs. Based on the Latin rectangle of the individual WBAN, each sensor is allocated a backup time-slot and a channel to use if it experiences interference such that collisions among different transmissions of coexisting WBANs are minimized. We further present a mathematical analysis that derives the collision probability of each sensor's transmission in the network. In addition, the efficiency of CHIM in terms of transmission delay and energy consumption minimization are validated by simulations.

Wireless sensor networks have become an attractive choice for many monitoring applications in an unattended setups; some of these applications such as pipeline, railroad and highways monitoring require the sensors to be placed in a linear topology. These sensors need to be placed as far apart while still maintaining a minimum required coverage in order to minimize the required node count and reduce cost. In this paper, we present three optimization models for determining the node density that varies in the objective. The first opts to achieve a desired level of detection fidelity while minimizing the number of deployed sensors. The second model considers the scenario with a constrained node count and determines the position of the available nodes such that the coverage is maximized. In the third model, we strive to minimize the number of deployed nodes when the desired fidelity is not uniform and some locations require higher coverage than others. The proposed optimization formulations are generic in nature and can be applied to any sensor coverage model. The three optimization models are validated through implementation using an attenuated disc coverage model where the detectability of a sensor is inversely proportional to the distance.

A Wireless Body Area Network (WBAN) provides health care services. The performance and utility of WBANs can be degraded due to interference. In this paper, our contribution for co-channel interference mitigation among coexisting WBANs is threefold. First, we propose a distributed orthogonal code allocation scheme, namely, OCAIM, where, each WBAN generates sensor interference lists (SILs), and then all sensors belonging to these lists are allocated orthogonal codes. Secondly, we propose a distributed time reference correlation scheme, namely, DTRC, that is used as a building block of OCAIM. DTRC enables each WBAN to generate a virtual time-based pattern to relate the different superframes. Accordingly, DTRC provides each WBAN with the knowledge about, 1) which superframes and, 2) which time-slots of those superframes interfere with the time-slots within its superframe. Thirdly, we further analyze the success and collision probabilities of frames transmissions when the number of coexisting WBANs grows. The simulation results demonstrate that OCAIM outperforms other competing schemes in terms of interference mitigation and power savings.

Wireless sensor networks (WSNs) may consist of a large number of resource-constrained sensor nodes. These nodes probe their surroundings and transmit their measurements over multi-hop routes to an in-situ Base-Station (BS). Given the critical role of the BS, it can be targeted by an adversary's attack. Basically, the adversary may intercept transmissions made by the individual nodes, even without being able to decode them, and apply techniques, such as Evidence Theory (ET), to uncover the routing topology. The fact that the BS is a sink for all data traffic makes it possible for the traffic analysis to reveal the location of the BS. This paper proposes a novel cross-layer approach to boost the BS anonymity by forming clusters and a cluster-mesh routing topology. Basically, a set of sensor nodes in a WSN are grouped into non-overlapping clusters, each is led by a cluster head (CH). A CH aggregates data from its cluster members. A mesh topology is then formed for all CHs and BS for dissemination of the aggregated data. The BS participates in the inter-CH packet forwarding so that it does not become distinguishable among the CHs. The simulation results confirm the effectiveness of the proposed approach.

In a Wireless Sensor Network (WSN) Nodes are distributed to monitor an area of interest such as a military battlefield, and transmit their data to in-situ Base-Station (BS) through multi-hop paths. In addition to collecting and processing the sensor data, the BS performs network management tasks. Due to such an important role, an adversary would try to locate the BS and target it with Denial-of-Service (DoS) attack in order to disrupt the WSN operation. Locating the BS can be done by intercepting transmissions and then employing traffic analysis techniques such as Evidence Theory (ET). To counter such an attack, this paper presents a novel technique for increasing BS anonymity by establishing a sleep/active schedule among the nodes that are far away from the BS, and increasing the traffic density in selected parts of the network in order to give the impression that the BS is located in the vicinity of the sleeping nodes. The simulation results confirm the effectiveness of the proposed technique in increasing the anonymity of the BS.

Wi-Fi Direct has been gaining popularity in recent years as a mean for device-to-device communications. Adopting such technology allows portable devices, such as smart phones, to form groups for data sharing without the need for any infrastructure support. Interconnecting multiple groups allows broader coverage and enables the use of Wi-Fi direct in applications such as disaster recovery. However, the current implementation of Wi-Fi direct in popular software environments, such as Android, lacks proper support for multi-group communications. One of the limitations in Android is that all created groups share the same IP subnet. Thus, even if multiple Wi-Fi Direct groups could be interconnected, still bidirectional communications between some devices would not be possible. In this paper, we propose an IP Subnet Negotiation Protocol for Seamless Multi-Group Communications (ISNP) to address such limitation. ISNP is composed of two modules, one at the application level and the other at the OS level that allow Wi-Fi Direct groups to have distinct IP subnets. ISNP can be integrated with other Wi-Fi Direct multi-grouping protocols to allow bidirectional links between groups. In addition, ISNP is flexible, as it allows devices with no modification to its OS to still participate in the protocol, and lightweight, as it does not force the devices to form any type of connection before running ISNP. ISNP is validated through implementation on Android devices. The validation results have confirmed ISNP's efficiency and its ability of avoiding conflicts between IP subnets.

Abstract In applications of wireless sensor networks (WSNs), all data packets are directed toward the base‐station (BS) over multi‐hop routes. In addition to data processing, the BS can interface the WSN to remote centers. Therefore, the failure of the BS diminishes the network utility and makes the data inaccessible. An adversary would thus try to uncover the BS's location by analyzing the traffic patterns of the network in order to launch denial of service attacks. For that reason, various countermeasure techniques have been proposed to boost the anonymity of the BS. In this paper, we study the existing traffic analysis models that an adversary could apply to locate the BS and point out shortcomings that make contemporary countermeasures weaker than thought. We further present a novel attack model that increases the adversary's confidence in localizing the BS while significantly reducing the traffic analysis complexity. Additionally, we introduce two novel anonymity metrics that can be used for evaluating the network resilience to attacks and for gauging the performance of countermeasures. The simulation results confirm the effectiveness of the proposed attack model. Copyright © 2017 John Wiley & Sons, Ltd.

Underwater Wireless Sensor Networks (UWSN) are faced with the challenge of estimating distances between nodes for localization purposes. To estimate the range, most researchers opt to model the propagation trajectory of an acoustics signal either as straight lines, or by solving the wave equation and then deriving the distance. The main drawback of the former is oversimplifying the aquatic propagation model by assuming a homogeneous medium, whereas the latter approach is computationally intense and requires knowledge of the water characteristics that alters the signal velocity along the propagation path; examples of which are the sound speed profile and density. Furthermore, ranging techniques for deep water applications differ from shallow water ones due to the different boundary conditions faced in each medium. In this paper, we propose a novel and unified ranging method that is suitable for both deep and shallow waters and implicitly factors the aquatic characteristics. In particular, the proposed method uses a parabolic ranging model that takes advantage of both angle of arrival and departure of newly formed communication links to relatively localize nodes within a network. Basically, we localize nodes by modeling the propagation between each pair of nodes as a parabola that satisfies the angles of arrival and departure and show that that parabolic expansion factors in the reflection, refraction and straight line model. We validate our approach through simulation and compare its performance to contemporary ranging techniques.

The use of wireless sensor networks (WSNs) can be advantageous in applications that serve in hostile environments such as security surveillance and military battlefield. The operation of a WSN typically involves collection of sensor measurements at an in-situ Base-Station (BS) that further processes the data and either takes action or reports findings to a remote command center. Thus the BS plays a vital role and is usually guarded by concealing its identity and location. However, the BS can be susceptible to traffic analysis attack. Given the limited communication range of the individual sensors and the objective of conserving their energy supply, the sensor readings are forwarded to the BS over multi-hop paths. Such a routing topology allows an adversary to correlate intercepted transmissions, even without being able to decode them, and apply attack models such as Evidence Theory (ET) in order to determine the position of the BS. This paper proposes a technique to counter such an attack by reshaping the routing topology. Basically, the nodes in a WSN are grouped in unevenly-sized clusters and each cluster has a designated aggregation node (cluster head). An inter-cluster head routes are then formed so that the BS experiences low traffic volume and does not become distinguishable among the WSN nodes. The simulation results confirm the effectiveness of the proposed technique in boosting the anonymity of the BS.

Energy scavenging from ambient sources represents a promising solution for sustaining continual operation for wireless sensor networks. An energy harvesting wireless sensor network (EH-WSN) can have two node types, harvesting enabled nodes (HNs) and non-harvesting enabled nodes (NHNs). In this paper, we consider the problem of achieving network longevity in EH-WSN when in-network data aggregation is used. The aim is to construct an aggregation tree that extends the network lifetime through reducing the overhead on NHN as much as possible. Two solutions are proposed; the first model the problem using a integer linear program, whereas the second solution uses minimum directed spanning tree. The objective of these protocols is to extend the network lifetime while reducing the runtime complexity. Both solutions are evaluated through extensive simulation experiments. The obtained results demonstrate their feasibility and ability in achieving the design goals.

In-network data aggregation is one of the popular optimization methodologies in the realm of wireless sensor networks (WSNs). To enable effective implementation, a routing tree is formed and the node transmissions are carefully scheduled to meet flow constraints. Minimizing the data delivery latency has been the most common objective of the data aggregation scheduling optimization. Prior work on this optimization problem pursued heuristics to overcome the complexity of the problem and used an upper bound on latency as a metric to assess the quality of the solution. In this paper we argue that for small and medium sized networks it is computationally feasible to obtain the optimal solution. We formulate the data aggregation scheduling problem as a linear integer program. Two variants of the problem are considered. The first assumes that the routing tree is predefined, e.g., through a network layer protocol, and node transmissions are to be scheduled to minimize delay. For the second variant, the routing topology formation and node schedule are to be optimized in an integrated manner. The proposed solutions are compared to the existing heuristics via extensive simulation experiments.

We consider networks where mobile sensor/actor nodes move to specific locations in order to conduct data collection or deliver a response to an event. The challenge is to find the best tour for the mobile nodes in order to visit the given set of locations. In this paper, the objective of the optimization is to extend the node lifetime by emphasizing both path efficiency and balanced energy consumption when identifying and assigning tours to mobile nodes. Compared to existing schemes in the literature, we consider the initial position of mobile sensors when determining the tours. We formulate the optimization as a balanced multi-salesman travel problem and propose a solution based on a two-step approach. First, we determine the shortest tour that includes all event locations by forming the Hamiltonian cycle. Then, we formulate the optimal partitioning of such a cycle as a linear program (LP) where the objective is to reduce the tour length while minimizing the maximum tour a node has to be make. For scalability and to expedite convergence, we propose a method for solving the LP formulation based on Branch & Price algorithm. The simulation results confirm the effectiveness of our optimization formulation and the advantage of our solution compared to competing schemes.

In most applications of wireless sensor networks (WSNs), nodes act as data sources and forward measurements to a central base station (BS) that may also perform network management tasks. The critical role of the BS makes it a target for an adversary's attack. Even if a WSN employs conventional security primitives such as encryption and authentication, an adversary can apply traffic analysis techniques to find the BS. Therefore, the BS should be kept anonymous to protect its identity, role, and location. Previous work has demonstrated distributed beamforming to be an effective technique to boost BS anonymity in WSNs; however, the implementation of distributed beamforming requires significant coordination messaging that increases transmission activities and alerts the adversary to the possibility of deceptive activities. In this paper we present a novel, cross-layer design that exploits the integration of the control traffic of distributed beamforming with the MAC protocol in order to boost the BS anonymity while keeping the rate of node transmission at a normal rate. The advantages of our proposed approach include minimizing the overhead of anonymity measures and lowering the transmission power throughout the network which leads to increased spectrum efficiency and reduced energy consumption. The simulation results confirm the effectiveness our cross-layer design.

In a wireless sensor network (WSN), nodes probe their surroundings and collaboratively perform situational assessment. Given the need for information sharing, maintaining inter-node connectivity is a primary requirement. However, the failure of a node due to energy depletion or physical damage may cause the network to get partitioned into disjoint segments and thus hinders data delivery and inter-node coordination and thus degrades overall network operation. This paper presents a Recovery algorithm that forms a topology with Increased Robustness against recurrent failure (RIR). RIR tolerates the failure of multiple connectivity-critical nodes through repositioning of healthy nodes. The approach favors substituting a failed node with one with the highest residual energy in order to sustain the network connectivity for the longest time. RIR models the recovery as a Minimum Cost Flow problem to determine the best set of node relocations for repairing the network topology while minimizing the motion overhead of the recovery process. The effectiveness of RIR is validated through simulation.

Provisioning network survivability is especially crucial in wireless sensor and actor network (WSAN) because nodes deployed in hostile environments are prone to frequent failures. Failure of an actor significantly impact actor connected coverage which is essential for effective network operation. Existing mobility-based recovery schemes are either geared towards restoring inter-actor connectivity or area coverage. None of them consider sustaining actor coverage (i.e., having sensors reachable to actors) while restoring inter-actor connectivity. This paper presents RACE, a novel mechanism to Restore Actor Connected Coverage with reduced recovery overhead. RACE distinguishes critical/non-critical actors based on 2-hop information to better assess the scope of the failure and optimize the recovery procedure. Neighbors of a failed actor employ a cooperative failure detection scheme and only perform a limited-scale network reconfiguration to adopt any bereaved sensors left unreachable (uncovered by an actor) due to failure of a non-critical actor. In case a critical actor fails, RACE substitutes it with a non-critical neighbor that has the least impact on coverage (i.e., number of sensors). If it is necessary to engage critical actors in the recovery, RACE is recursively applied by relocating actors until a non-critical node is picked. Simulation results confirm the performance advantage of RACE compared to the best contemporary schemes.

Area-based localization is a simple and efficient approach, where each node estimates its position based on proximity information to some special nodes with known location, called anchors. Based on the anchors' coordinates, each node first determines its residence area and then approximates its position as the centroid of that area. Therefore, the accuracy of the estimated position depends on the size of the residence area; the smaller the residence area is, the better the accuracy is likely to be. Because the size of the residence area mainly depends on the number and the positions of anchor nodes, their deployment should be carefully considered in order to achieve a better accuracy while minimizing the cost. For this purpose, in this paper we conduct a theoretical study on anchor placement for a very popular area based localization approach. We determine the optimal anchor placement pattern for increased accuracy and how to achieve a particular accuracy goal with the least anchor count. Our analytical results are further validated through simulation.

In Vehicular Ad Hoc Networks (VANETs), the roadside units (RSUs) need to know the number of vehicles in their vicinity to be used in traffic management. However, an attacker may launch a Sybil attack by pretending to be multiple simultaneous vehicles. This attack is severe when a vehicle colludes with others to use valid credentials to authenticate the Sybil vehicles. If RSUs are unable to identify such an attack, they will report wrong number of vehicles to the traffic management center, which may result in disseminating wrong traffic instructions to vehicles. In this paper, we propose a cross-layer scheme to enable the RSUs to identify such Sybil vehicles. Since Sybil vehicles do not exist in their claimed locations, our scheme is based on verifying the vehicles' locations. A challenge packet is sent the vehicle's claimed location using directional antenna to detect the presence of a vehicle. If the vehicle is at the expected location, it should be able to receive the challenge and send back a valid response packet. In order to reduce the overhead and instead of sending challenge packets to all the vehicles all the time, packets are sent only when there is a suspicion of Sybil attack. We also discuss several Sybil attack alarming techniques. The evaluation results demonstrate that our scheme can achieve high detection rate with low probability of false alarm. Additionally, the scheme requires acceptable communication and computation overhead.

With the large increase in the number of registered vehicles, the congestion and slow traffic problems are expected to worsen. Vehicular Ad Hoc Networks (VANETs) can play a great role in avoiding these problems by sending guidance to vehicles to pursue alternative routes. However, the published schemes require vehicles to report their future routes which can seriously violate privacy. In this paper, we present privacy-preserving route reporting scheme that suits VANET-enabled traffic management rather than warning vehicles after congestion happens. Vehicles provide encrypted segment-based route information to road side units (RSUs). Instead of sending one message for each route segment, all the segments' data can be collected by one message using homomorphic encryption. RSUs compute the encryption of the expected number of vehicles in each segment of the road without knowing the actual routes of vehicles. Each RSU shares the vehicles' routes information with a traffic management center (TMC), which decrypts the expected total number of vehicles at different segments of the road without knowing the individual vehicles' routes. Then, it conducts analysis and sends predictions and recommendations back to the RSUs. Passing vehicles solicit hints from RSUs on the expected traffic condition in order to decide to take an alternating route if there is a potential of congestion or slow traffic in its main route. Our analysis and evaluation results demonstrate that our scheme can preserve the privacy of the drivers' future routes in an efficient and secure way.

In certain situations, there may be an urgent need to disseminate alerts without the support of communication infrastructure. For example, when a disaster like an earthquake strikes, it may cause the loss of cellular coverage, Wi-Fi hotspots, etc. In this and similar application scenarios, last mile connectivity will be needed for prompt delivery of alerts. Nearly everyone nowadays carry a smart device that is equipped with at least three communication options that do not need any infrastructure, namely, Bluetooth, Wi-Fi Ad-hoc, and Wi-Fi Direct. Despite their ability to work without infrastructure, all these options need a connection setup phase before starting data dissemination. The setup phase incurs unneeded overhead and delay, which diminish their utility in delivering time-critical data. In this paper, we propose a protocol that uses the service discovery mechanism in Wi-Fi Direct to addresses these shortcomings. The protocol embeds the alert data to be disseminated in a Wi-Fi Direct service discovery frame and stores it locally. Other devices interested in receiving alerts send service discovery requests to get updates. The protocol also takes care of forwarding the received alerts to other devices as well. The evaluations showed that the proposed protocol is capable of transmitting timely alerts.

Wireless ad hoc networks have become valuable assets to both the commercial and military communities with applications ranging from industrial control on a factory floor to reconnaissance of a hostile border. In most applications, nodes act as data sources and forward information to a central base station (BS) that may also perform network management tasks. The critical role of the BS makes it a target for an adversary's attack. Even if an ad hoc network employs conventional security primitives such as encryption and authentication, an adversary can apply traffic analysis techniques to find the BS. Therefore, the BS should be kept anonymous to protect its identity, role, and location. Previous work has demonstrated distributed beamforming to be an effective technique to boost BS anonymity in wireless ad hoc networks; however, the increased anonymity and corresponding energy consumption depend on the quality and quantity of selected helper relays. In this paper we present a novel, distributed approach for determining a set of relays per hop that boosts BS anonymity using evidence theory analysis while minimizing energy consumption. The identified relay set is further prioritized using local wireless channel statistics. The simulation results demonstrate the effectiveness our approach.

In recent years, Wireless Sensor Networks (WSNs) have become valuable assets to both the commercial and military communities with applications ranging from industrial control on a factory floor to reconnaissance of a hostile border. In most applications, the sensors act as data sources and forward information generated by event triggers to a central sink or base station (BS). The unique role of the BS makes it a natural target for an adversary that desires to achieve the most impactful attack possible against a WSN with the least amount of effort. Even if a WSN employs conventional security mechanisms such as encryption and authentication, an adversary may apply traffic analysis techniques to identify the BS. This motivates a significant need for improved BS anonymity to protect the identity, role, and location of the BS. Previous work has proposed anonymity-boosting techniques to improve the BS's anonymity posture, but all require some amount of overhead such as increased energy consumption, increased latency, or decreased throughput. If the BS understood its own anonymity posture, then it could evaluate whether the benefits of employing an anti-traffic analysis technique are worth the associated overhead. In this paper we propose two distributed approaches to allow a BS to assess its own anonymity and correspondingly employ anonymity-boosting techniques only when needed. Our approaches allow a WSN to increase its anonymity on demand, based on real-time measurements, and therefore conserve resources. The simulation results confirm the effectiveness of our approaches.

Wireless Sensor Networks (WSNs) have become an attractive choice for many applications that serve in hostile setup. The operation model of a WSN makes it possible for an adversary to determine the location of the base-station (BS) in the network by intercepting transmissions and employing traffic analysis techniques such as Evidence Theory. By locating the BS, the adversary can then target it with denial-of-service attacks. This paper promotes a novel strategy for countering such an attack by adaptively combining packet payloads. The idea is to trade off packet delivery latency for BS location anonymity. Basically, a node on a data route will delay the forwarding of a packet until one or multiple additional packets arrive and the payloads are then combined in a single packet. Such an approach decreases the number of evidences that an adversary will collect and makes the traffic analysis inclusive in implicating the BS position. Given the data delivery delay that will be imposed, the proposed technique is to be adaptively applied when the BS anonymity needs a boost. The simulation results confirm the effectiveness of the proposed technique.

Robotics network is an effective way to deploy in areas where human intervention is impossible or possess some risks. In rescue operations, for example, robots can be used to help in discovering bodies under the rubbles or even assist the injured. One of the main challenges in these applications is how to deploy the robots in the absence of GPS services and without central coordination. In this paper, we tackle such a challenge in scenarios where landmarks are present in the deployment area. The problem is modeled by defining the actor coverage based on the individual landmarks. To deal with actor count limitation, a spatial-temporal coverage solution is pursued where some actors juggle between landmarks as needed. We formulate such dynamic coverage problem using Potential Fields where landmarks and actors exert virtual forces based on coverage demand and overlap. Extensive simulation experiments have been carried out using NS3 to evaluate the proposed approach and to compare it to Random Waypoint based dynamic coverage method. The simulation results validate the distinct performance of the approach in term of demand satisfaction and average traveled distance.

In applications of wireless Sensor Networks (WSNs), the base-station (BS) acts as a sink for all data traffic. The continuous flow of packets toward the BS enables the adversary to analyze the traffic and uncover the BS position. In this paper we present a technique to counter such an attack by morphing the traffic pattern in the WSN. Our approach introduces multiple fake sinks and deceptive relays so that nodes other than the BS are implicated as the destination of all data traffic. Since the problem of the optimal fake sink's placement is NP-hard, we employ a heuristic to determine the most suitable fake sink count and placement for a network. Dynamic load-balancing trees are formed to identify relay nodes and adapt the topology to route packets to the faked (and real) sinks while extending the network lifetime. The simulation results confirm the effectiveness of the proposed technique.

A growing interest in Internet of things (IoT) paradigm has enabled a wide range of physical objects and environments to be monitored in fine detail by using pervasive sensing and communication devices. In this paper, we develop an IoT framework that targets one of the biggest challenges in modern cities, namely, Parking Management. Populating parking facilities with sensors imposes unwarranted installation and maintenance costs. In our system we eliminate the additional sensing or monitoring facilities, instead use participatory sensing paradigm to find, monitor and regulate parking. In our system volunteers collect and share information from their local environment using smart-devices. We link the user reputation scores to the reliability of user-provided data and determine parking availability and combine them with a reward model for incentives. The validation results through simulated as well as real participatory users have shown that our approach can yield accurate parking spot availability using user provided data.

Wi-Fi Direct (WFD) has become almost a standard for peer-to-peer networking on smart devices. Typically, WFD allows a user to search for and connect to other devices, which results in creating a group of devices that can exchange data. In certain applications like alert dissemination in disaster areas, it is required to form groups dynamically and to share data across the group boundaries. However, these capabilities are not offered by the current WFD implementations. In this paper, we propose EMC, a novel protocol that allows WFD devices to dynamically cluster themselves into groups and appropriately elect group owners. EMC also allows members of distinct groups to communicate. EMC is validated through implementation on Android devices and through the realization of a chat application.

Area-based localization algorithms use only the position of some reference nodes, called anchors, to estimate the residence area of the remaining nodes. Existing algorithms use a triangle, a ring or a circle as the geometric shape that defines the node's residence area. However, existing algorithms suffer from two major problems: (1) in some cases, they might make wrong decisions about a node presence inside a given area, or (2) they require high anchor density to achieve a low location estimation error and high ratio of localizable nodes. In this paper, we overcome these shortcomings by introducing a new approach for determining the node's residence area that is geometrically shaped as a half-symmetric lens. A novel half symmetric lens based localization algorithm (HSL) is proposed. HSL yields smaller residence areas, and consequently, better location accuracy than contemporary schemes. HSL further employs Voronoi diagram in order to boost the percentage of localizable nodes. The performance of HSL is validated through mathematical analysis, extensive simulations experiments and prototype implementation. The validation results confirm that HSL achieves better location accuracy and higher ratio of localizable nodes compared to competing algorithms.

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

Decreasing costs and increasing functionality of embedded computation and communication devices have made Wireless Sensor Networks (WSNs) attractive for applications that serve in inhospitable environments like battlefields, planetary exploration or environmental monitoring. WSNs employed in these environments are expected to work autonomously and extend the network lifespan for as long as possible while carrying out their designated tasks. The harsh environment exposes the individual nodes to high risk of failure, which can potentially partition the network into disjoint segments. Therefore, the network must be able to self-heal and restore lost connectivity using available resources. The ad-hoc nature of deployment, harsh operating environment and lack of resources makes distributed approaches the most suitable choice for recovery. In this paper we present SSBR, a straight skeleton based distributed approach for reconnecting a WSN partitioned into disjoint segments. The basic idea of SSBR is to decompose the network area into two dimensional set of paths that can be used for recovery. Mobile nodes are deployed by surviving disjoint segments along the paths until connectivity is reestablished. The performance of SSBR is validated through mathematical analysis and simulation.

In wireless sensor networks (WSNs), it may be necessary to have a unified time reference for all network nodes. Such a necessity may be imposed by the management strategy in the network, e.g., using time based medium access arbitration, or simply due to the dynamic nature of the application, e.g. target tracking. Since each node more or less operates autonomously, the clocks of the individual nodes have to be synchronized. Contemporary clock synchronization protocols introduce significant messaging overhead and thus do not suit the resource-constrained WSNs. In the paper, we propose a novel solution called Synchronization through Piggybacked Reference Timestamps (SPiRT). SRiRT exploits the popularity of two-tier network architectures in WSN, where nodes are grouped into disjoint clusters and each cluster is lead by a cluster-head that aggregates the data from its members. Each cluster-head synchronizes its clock to that of a reference node in the network through message exchange. Since cluster members can overhear the cluster-head transmissions, SPiRT takes advantages of such synchronization traffic to adjust the clock of the cluster members. SPiRT calls for appending the reference timestamps in the cluster-head messages so that a cluster member can estimate their clock adjustment. This cuts on energy consumption and increases the synchronization efficiency of SPiRT. SPiRT is validated through simulation and implementation on a Micaz based testbed. The validation results confirm the effectiveness of SPiRT and show that it outperforms competing schemes in the literature.

Wireless sensors and actors networks (WSANs) have the capacity for not only monitoring some phenomena through sensor nodes but also performing appropriate actions. Most of the contemporary WSAN management solutions focus on defining communication path among sensors and actors and on tasking appropriate actors to handle the detected events. In this paper we classify events based on how they evolve over time into continuous and discrete and categorize the WSAN management strategies accordingly. Unlike discrete events, a continuous event spreads quickly and becomes more serious as time passes. Such a characteristic introduces more challenges and motivates a non-conventional management strategies. This paper presents an approach for Sensor-Actuator Coordination for Handling Spreading events (SACHS). SACHS opts to enable the network to respond quickly in order to avoid the event from growing in scope, e.g., prevent a fire from spreading, while reducing the energy overhead due to the coordination messages and due to actor's relocation to the event region. SACHS limits sensor-actor and actor-actor interactions and exploits local sensor-sensor communication to determine the scope of the event, define spots for actors to position at, and schedule the actors' response. The simulation results confirm the performance advantage of SACHS compared to competing schemes.

Wireless Sensor Networks (WSNs) are usually constrained energy and bandwidth. Many solutions, like network clustering, have been proposed in order to overcome these limitations. While this solution is deemed efficient, the cluster-heads closer to the base-station would forward more data packets than farther ones, and thus their energy drains at a faster rate. In this paper, we propose an Energy- and Proximity-based Unequal Clustering algorithm (EPUC) to solve this problem. Basically EPUC imposes a condition on the distance among cluster-heads that is adaptively adjusted, so that the inter-cluster-head proximity is smaller as they get closer to the base-station. In addition, the cluster population is set while factoring in the inter-cluster relaying activities in order to balance the load on cluster-heads. We evaluate the performance of EPUC through simulation and confirm its effectiveness of EPUC using network lifetime metrics.

The use of smart portable devices has become very popular in the society nowadays. Many of these devices are equipped with sensors that can provide a wealth of information about the surroundings once their readings are aggregated. These capabilities have fueled interest in employing these devices in emerging unconventional applications such as crowdsourcing and vehicular networking, where sensors data is shared for better situational awareness, managing road congestion, coordinating disaster recovery, criminals hunting, etc. Internetworking of collocated devices in these applications enables aggregation of sensor readings and decreasing the demand on the carrier's communication resources, which translate to lower cost, better bandwidth utilization and increased user participation. Wi-Fi Direct is one of the most promising peer-to-peer technologies that can enable such local internetworking of devices. Wi-Fi Direct transceivers are available on almost all new smart devices. However, the software support for Wi-Fi Direct is quite limited. This paper highlights limitations in Android based platforms and presents a framework for supporting formation and management of groups of communicating devices. The proposed framework is validated through the implementation of a chat application over multiple Android based devices.

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

Most communication and localization algorithms in underwater environments have been constrained by dependencies on the Line Of Sight (LOS), which is hard to guarantee due to the inherent node mobility. This constraint hinders node discovery and ad hoc formation in underwater networks and limits the performance of routing protocols. This article introduces a novel Surface-Based Reflection (SBR) model that uses a homomorphic deconvolution technique to establish water-surface-reflected communication links. We then propose a Surface-Based Reflection Anchor-free Localization (SBR-AL) algorithm that can be employed by the individual nodes to establish a relative coordinate system. Our approach also employs a switch-beamed directional antenna model that allows each node to use the LOS estimated from SBR-AL to enable directional communication which is beneficial for higher Signal-to-Noise Ratios (SNR). The relative locations can facilitate the various network operation functions such as geo-routing and collision-free medium access. The simulation results confirm the effectiveness of the proposed approach.

Due to low-risk and cost-effectiveness, Wireless Sensor Networks (WSNs) have become the primary choice for serving in inhospitable environments like battlefields or security surveillance. In these application setups, nodes operate in harsh conditions and become susceptible to failure. In addition, the environment makes it dangerous and sometime impossible to replace a node that depletes its energy or gets damaged. When multiple nodes fail at the same time the network may get partitioned into disjoint segments and its service may significantly degrade or even cease. Therefore, the network must self-heal using existing resources. The major loss of connectivity and the lack of centralized control leave distributed recovery procedures as the most appropriate option for recovery. In this paper we present DarDs, a distributed approach for reconnecting disjoint segments. The basic idea is to determine the position of the fewest relay nodes that enable the network to restore connectivity. Then, nodes are moved from the individual segments to the designated relay positions such that the total travel overhead is minimized. The performance of DarDs is validated through mathematical analysis and simulation.

In wireless sensor networks, in-network data aggregation filters out redundant sensor readings in order to reduce the energy and bandwidth consumed in disseminating the data to the base-station. In this paper, we investigate the problem of reliable collection of aggregated data with minimal latency. The aim is to form an aggregation tree such that there are k disjoint paths from each node to the base-station and find a collision-free schedule for node transmissions so that the aggregated data reaches the base-station in minimal time. We propose a novel algorithm for Reliable and Timely dissemination of Aggregated Data (RTAD). RTAD intertwines the formation of the aggregation tree and the allocation of time slots to nodes, and assigns parents to the individual nodes in order to maximize time slot reuse. The simulation results show that RTAD outperforms competing algorithms in the literature.

Wireless Sensor Networks (WSN) that operate unattended in harsh environments are susceptible to large scale damage, where many nodes fail simultaneously and the network gets partitioned into several disjoint segments. Restoring connectivity of structurally damaged WSN's segments may be urgent considering that they are employed to assist in risky missions. To deal with these scenarios, Mobile Data Mules (MDMs) are employed to establish intermittent links by moving and carrying data between pairs of segments on an inter-segment minimum spanning tree (mst). In this paper, we study a constrained version of the federation problem when the number of MDM's “k” is less than the number of edges on an mst, which makes the problem more challenging. We present a novel algorithm that groups the segments into k overlapping clusters based on the intersegment proximity. Each cluster is assigned a distinct MDM to tour its segments. A segment that belongs to two clusters serves as a gateway that enables data transfer across clusters. Our algorithm minimizes the tour length for each MDM and sets the speed of the individual MDMs to rendezvous at gateway segments so that buffering space and time for inter-cluster traffic are minimized. The simulation results confirm the effectiveness of our algorithm.

Disjoint Wireless Sensor Networks (WSNs) can be reconnected by placing additional relay nodes in the damaged areas. However, in some cases there may not be enough relays to reconnect all the partitions with the sink node. In such a case, some of the relays can exploit their motion capabilities and temporarily act as a mobile data collector (MDC) between partitions providing intermittent connectivity for the nodes sitting in those partitions. Nonetheless, due to increased data latency intermittent connectivity creates, the number of such MDCs need to be minimized. On the other hand, given that the energy resources for an MDC is limited, an upper bound on the travel distance overhead for an MDC needs to be imposed. This paper proposes a relay placement algorithm which guarantees connectivity by maximizing the number of stable connections while meeting the maximum tour constraint on the MDCs. The approach first determines the number and location of relays to restore connectivity by establishing stable links using a Steiner Minimum Tree (SMT) heuristic. Assuming that the number of available relays is less than the needed count, the algorithm determines how many of the available relays need to be stationary and how many of them should act as MDCs. By initially assuming all relays as MDCs, an iterative procedure is followed to reduce the MDC count while meeting the maximum tour length constraint. Specifically, groups of partitions are created and assigned to MDCs for touring. The proposed approach is validated with extensive simulations under a variety of conditions.

Wireless sensor networks (WSNs) are used to continuously monitor certain area of interest and send data back to the base station for processing. In many applications, WSNs serve in inhospitable environments where multiple node failure may take place causing the network to be divided into disjoint segments. Also, multiple standalone WSNs in some applications may need to be federated to collectively handle an important event that requires data sharing among these networks. A viable approach for establishing connectivity among these network segments is by employing mobile data collectors (MDCs). Few MDCs can be used to create intermittent links among the segments by touring and carrying data. Obviously, the travel path of the MDCs will affect the date delivery latency. In this paper, we present an algorithm to form an intermittent star topology so that the average and maximum delay for delivering the inter-segment traffic is reduced. The performance of the algorithm is validated through simulation.

In recent years, Wireless Sensor Networks (WSNs) have become valuable assets to both the commercial and military communities with applications ranging from industrial control on a factory floor to reconnaissance of a hostile border. In most applications, the sensors act as data sources and forward information generated by event triggers to a central sink or base station (BS). The unique role of the BS makes it a natural target for an adversary that desires to achieve the most impactful attack possible against a WSN with the least amount of effort. Even if a WSN employs conventional security mechanisms such as encryption and authentication, an adversary may apply traffic analysis techniques to identify the BS. This motivates a significant need for improved BS anonymity to protect the identity, role, and location of the BS. In this paper we propose a novel cross-layer relay-selection algorithm and a distributed beamforming protocol to increase BS anonymity. We examine the effect of the proposed distributed beamforming technique on improving BS anonymity using evidence theory analysis and demonstrate the effectiveness of this approach through simulation.

In-network data aggregation is often pursued to remove redundancy and correlate the data en-route to the base-station in order to save energy in wireless sensor networks (WSNs). In this paper, we present a novel cross-layer approach for reducing the latency in disseminating aggregated data to the base-station over multi-frequency radio links. Our approach forms the aggregation tree with the objective of increasing the simultaneity of transmissions and reducing buffering delay. Aggregation nodes are picked and time-slots are allocated to the individual sensors so that the most number of ready nodes can transmit their data without delay. Colliding transmissions are avoided by the use of different radio channels. Our approach is validated through simulation and is shown to outperform previously published schemes.

Ensuring area coverage is one of the key requirements of wireless sensor networks (WSNs). When nodes are randomly placed in the area of interest, redundancy is often provisioned in order to lower the probability of having voids, where part of the area is not within the detection range of any sensor. To extend the lifetime of the network, a duty cycle mechanism is often applied in which only a subset of the nodes are activated at a certain time while the other nodes switch to low-power mode. The set of active nodes are changed over time in order to balance the load on the individual sensors. The selection of active nodes is subject to meeting the coverage requirement. Assessing the coverage of a sensor is based on knowing its position. However, localization schemes usually yield a margin of errors which diminishes the coverage fidelity. Conservative approaches for mitigating the position inaccuracy assume the worst-case error across the network and end up activating excessive number of nodes and reduces the network lifetime. In this paper, we present an approach for estimating a bound on the maximum error for the position of each sensor and propose a distributed algorithm for achieving high fidelity coverage while engaging only a subset of the sensors. The simulation results confirm the performance advantages of our approach.

Wireless Sensor Networks (WSNs) often serve mission-critical applications in inhospitable environments such as battlefield and territorial borders. Inter-node communication is essential for WSNs to effectively fulfill their tasks. In hostile setups, the WSN may be subject to damage that breaks the network connectivity and disrupts the application. The network must be able to recover from the failure and restore connectivity so that the designated tasks can be carried out. Given the unattended operation of the network, the recovery should be performed autonomously. In this paper we present a distributed algorithm for Autonomous Repair (AuR) of damaged WSN topologies in the event of multiple node failures. AuR models connectivity between neighboring nodes as electrostatic interaction between charges based on Coulomb's law. The recovery process is initiated locally at the neighbors of failed nodes by moving in the direction of loss to reconnect with other nodes. The performance of AuR is validated through simulation.

Relay node placement in wireless sensor networks has gained importance due to its potential use in prolonging network life time, reducing data latency, and establishing connected topologies. In this paper we studied the relay node placement problem to establish multi-hop communication paths between every pair terminals (i.e., sensors) where each hop in the path is less than a common communication range. Such a problem is defined as Steiner Tree problem with minimum Steiner points and Bounded Edge-Length problem which is known to be NP-Hard. This paper presents a novel relay node placement heuristics called Incremental Optimization based on Delaunay Triangulation (IO-DT). The algorithm takes advantage of feasibility of finding optimal solution for the case of three terminals. IO-DT calculates the Delaunay triangulation (DT) of terminals and iterates over the formed triangles. In each iteration the algorithm steinerizes a triangle as part of the final topology if selecting such a triangle provides a reduction in total number of relay node required as compared to the minimum spanning tree (mst) based approach. The time complexity of IO-DT is quadratic in the number of terminals, which is superior to competing schemes. The performance of the algorithm is validated through simulation.

Wireless Sensor Networks (WSNs) often operate in inhospitable environments to serve mission-critical and security-sensitive applications that involve hostile adversaries. These adversaries are eager to disrupt the WSN operation. Given the important role that the base-station (BS) plays in a WSN, the adversary opts to identify the BS and determine its location in order to damage the BS or launch a targeted denial of service attack. Therefore, maintaining the BS anonymity is of utmost importance in WSNs. Even if the adversary cannot decode packets, correlating the intercepted transmission through traffic analysis can reveal the position of the BS. This paper considers setups in which the network has multiple base-stations and proposes a novel approach in which these base-stations collaborate on confusing the adversary and averting attacks. The proposed Multi-player Anonymity optimization Game theoretic (MAG) approach calls for the introduction of inter-BS deceptive traffic and use game theory to determine the volume and destination of such traffic so that the variance in the location anonymity over all BSs is reduced. The simulation results demonstrate the effectiveness of MAG.

The 3-D nature of the underwater environment has made geographical-routing a popular choice in underwater acoustic sensor networks (UW-ASNs). A geographical (geo) routing protocol works by using the position information to find the best route from a source to a destination. These algorithms, often try to minimize the line-of-sight (LOS) Euclidean distance between hops, which is not always possible due to blocked LOS paths resulting in voids (or local minimum). Thus, traditional geo-routing algorithms typically employ a face routing recovery scheme that allows for back-tracking when blocked LOS links are encountered, which adds delay to the data delivery time. To overcome this, we propose a geo-routing scheme that does not depend on the LOS and utilize directional antennas to incorporate surface-reflected non-line-of-sight (NLOS) links in the routing process. Furthermore, the proposed algorithm is configured to optimize the route selection to achieve maximum network throughput. Simulation results are provided to validate the performance of the proposed algorithm.

Traffic congestion has become a daily problem that most people suffer. This not only impacts the productivity of the population but also poses a safety risk. Most of the technologies for intelligent highways focus on safety measures and increased driver awareness, and expect a centralized management for the traffic flow. This paper presents a new approach for enabling autonomous and adaptive traffic management through vehicular networks. By allowing data exchange between vehicles about route choices, congestions and traffic alerts, a vehicle makes a decision on the best course of action. Unlike centralized schemes that provide recommendations, our VANET-based Autonomous Management (VAM) approach factors in the destination and routes of nearby vehicles in deciding on whether rerouting is advisable. In addition, VAM leverages the presence of smart traffic lights and enables coordination between vehicles and lightcontrollers in order to ease congestion. The collective effect of all vehicles will be an autonomous reshape of the traffic pattern based on their destinations and road conditions. The simulation results demonstrate the advantage of VAM.

This position paper introduces a novel wireless sensor and actor network (WSAN) framework for autonomous monitoring and maintenance of pipe and power line (oil, gas, water, electricity) infrastructures in an efficient and cost-effective manner. The main focus is on boosting the availability of lifeline infrastructures through advancements in the WSAN technology. First, we categorize and classify the existing lifeline monitoring systems. Second, we identify the requirements for effective and efficient monitoring and maintenance of lifeline infrastructures. Third, we propose a novel WSAN architecture that combines sensing with distributed decision-making and acting capabilities through advanced robotics. Two operational models for the proposed architecture are also presented. The first is a push-up model that employs low-cost, multi-functional sensors along the lifeline to observe certain phenomena of interest, e.g., leakage, ruptures, clogs, etc., in real time and reports to actors over wireless links. The actors process the received data, coordinate with each other in order to identify the most appropriate response. The second is a pull-down model that capitalizes the resources of elite nodes (i.e. actors) in the network.

Due to harsh environmental conditions a Wireless Sensor Network (WSN) may suffer from large scale damage where many nodes fail simultaneously and thus the network gets partitioned into several disjoint network segments. Restoring intersegment connectivity is essential to avoid negative effects on the application. Employing mobile data collectors (MDCs), which by repositioning-facilitate the establishment of communication links between segments, may provide flexible solution to this problem. However the problem of finding shortest tours for MDCs is NP-Hard. In this paper we study the problem under constrained number of MDCs which makes the problem more challenging. We present a polynomial time heuristic for Interconnecting Disjoint Segments with k MDCs (IDM-kMDC). IDM-kMDC opts to minimize the tour lengths and balance the load on the k available MDCs. We model each segment by a representative. The IDM-kMDC heuristic finds k-subsets of representatives, computes an optimized tour for each subset and assigns one MDC for each tour. The performance of the algorithm is validated through simulation.

In recent years, Wireless Sensor Networks (WSNs) have become valuable assets to both the commercial and military communities with applications ranging from industrial automation on a factory floor to reconnaissance of a hostile border. In both examples, the sensors act as data sources and relay information to a central sink or base station (BS). Consider the possible attacks that an adversary could launch against a WSN once the BS is identified. This motivates a significant need for improved network security to protect against such malicious attacks. One such mitigation strategy is to increase the anonymity of the BS such that the adversary is unable to distinguish it from the other nodes of the sensor field. Many techniques have been proposed in the literature to improve BS anonymity by altering network-layer routing algorithms; however, few techniques have been proposed to improve anonymity at the Physical Layer (PHY). This is, in part, due to the lack of metrics to characterize PHY anonymity. In this paper, we propose an algorithm called “Intercept, Correlate, and Follow” (ICF) to characterize PHY anonymity in a WSN network. Simulation results based on the IEEE 802.15.4a Ultra Wideband (UWB) PHY illustrate the effectiveness of the proposed ICF algorithm.

In many applications need arises to connect a set of disjoint nodes or segments. Examples include repairing a partitioned network topology after the failure of multiple nodes, federating a set of standalone networks to serve an emerging event, and forming a strongly connected topology for a sparsely located data sources. Contemporary solutions for interconnecting these disjoint segments/nodes either deploy stationary relay nodes (RN) to form data paths or employ one or multiple mobile data collectors (MDCs) that pick packets from sources and transport them to destinations. The RN-based solution is preferred since it establishes permanent links as opposed to the intermittent links provided by the MDCs. In this paper we investigate the interconnection problem when the number of available RNs is insufficient for forming a stable topology and a mix of RNs and MDCs is to be used. We present an algorithm for determining where the RNs are to be placed and planning optimized travel routes for the MDCs so that the data delivery latency as well as the MDC motion overhead are minimized. The performance of the algorithm is validated through simulation.

In wireless sensor networks, all data packets are routed from the individual sensor nodes towards an in-situ base-station (BS). Such traffic pattern makes the BS vulnerable to adversary's attack. Basically, an adversary would intercept the ongoing transmissions and localize their sources. Then by employing traffic analysis techniques, an adversary would correlate the intercepted transmissions to uncover the data path which may lead to the location of the BS. Evidence theory is a well-known scheme that an adversary might use for traffic analysis. However, prior work considered only intercepted transmissions as evidences in the correlation process without factoring in the time of interception. In this paper, we argue that time-based correlation increases the accuracy of the traffic analysis and makes contemporary countermeasures ineffective. A novel technique is proposed to counter the time correlation and boost the anonymity of the BS. The technique imposes buffering delay at each relaying node on the data route in order to disturb the time correlation among consecutive transmissions. Our technique is validated through simulation.

The current state of the art in wireless sensor nodes, both in academia and industry, is a fractured landscape of designs mostly addressing individual problems. The most common commercial design derives directly from a mote developed at the University of California, Berkeley around 1999, and presents only moderate, incremental improvements over the original design. No designs yet present a comprehensive, intelligent solution befitting a modern system. By using dynamic power management, deep system configurability, autonomous peripheral modules, and multiple CPU architectures, this paper presents a flexible and efficient node architecture. Modules on a sensor node communicate with each other to coordinate their activities and power levels. Special attention is given to power sourcing and distribution. The platform may be configured to efficiently work with most networks, sensor types and power sources due to its improved connectivity and hierarchical design. The resulting Configurable Sensor Node (CoSeN) architecture is competitive with existing designs on price, size and power while greatly exceeding most of them on performance, configurability and application potential. CoSeN is validated through prototype implementation.

Nodes in wireless sensor networks continuously monitor their surroundings and report to the base station if there is any anomaly. WSN in extreme environmental conditions can suffer from a large scale failure where multiple nodes fail simultaneously and the network gets partitioned into disjoint segments. In addition, in some application multiple standalone WSNs need to be federated to collectively handle an emerging event that requires data sharing among these networks. Numerous approaches have been proposed in the literature to establish connectivity among these network segments by deploying stationary relay nodes (RN) to form data path. In scenarios where the available resources are limited, one or very few mobile data collectors (MDCs) can be used to create intermittent links among the disjoint segments. In this paper, we investigate a federation problem in scenarios where the number of available MDCs is less than the number of RNs required and more than the number of network segments. We present an algorithm to find an optimized travel routes for the MDCs so that the average delay of the network is minimized. The performance of the algorithm is validated through simulation.

Underwater communication relies on acoustic links due to its long propagation and low attenuation properties. Despite the advantages of using acoustic links over radio frequency (RF), the major drawback is the limited bandwidth of the acoustic spectrum. To circumvent this, we advocate the use of directional antennas rather than the traditional omni-directional antennas to boost the network throughput by taking advantage of the spatial spectrum. In this paper, we analyze the network throughput for shallow water communication with directional antennas. Unlike traditional line-of-sight (LOS) directional communication, our analysis will aim to factor in non-line-of-sight (NLOS) links in the throughput analysis. We will be focusing on two NLOS links; namely refracted-surface-reflected (RSR) and refracted-bottom-reflected (RBR) to study the multipath effects on the network throughput. Simulation experiments are also provided to validate the analytical results.

Ad-hoc networks are becoming an effective tool for many mission critical applications such as troop coordination in a combat field, situational awareness, etc. These applications are characterized by the hostile environment that they serve in and by the multicast-style of communication traffic. Therefore, authenticating the source and ensuring the integrity of the message traffic become a fundamental requirement for the operation and management of the network. However, the limited computation and communication resources, the large scale deployment and the unguaranteed connectivity to trusted authorities make known solutions for wired and single-hop wireless networks inappropriate. This paper presents a new Tiered Authentication scheme for Multicast traffic (TAM) for large scale dense ad-hoc networks. TAM combines the advantages of the time asymmetry and the secret information asymmetry paradigms and exploits network clustering to reduce overhead and ensure scalability. Multicast traffic within a cluster employs a one-way hash function chain in order to authenticate the message source. Cross-cluster multicast traffic includes message authentication codes (MACs) that are based on a set of keys. Each cluster uses a unique subset of keys to look for its distinct combination of valid MACs in the message in order to authenticate the source. The simulation and analytical results demonstrate the performance advantage of TAM in terms of bandwidth overhead and delivery delay.

Sensors in wireless sensor networks probe their surroundings and send their findings to a nearby base-station over multi-hop routes. In a hostile application environment the network may be attacked. Since the base-station plays a critical role and acts as a sink for all data traffic, it may be subject of pointed attack from an adversary that opts to inflict the most impact on the network. Despite of all physical precautionary measures for hiding the base-station, traffic analysis techniques can uncover its location since it is the end point of all data routes. This paper presents a novel distributed algorithm that boosts the anonymity of the base-station. A local sniffer adversary model is assumed where the source of a transmission is determined and used to search for the next hop until reaching the base-station. To counter such an attack, sensors in the vicinity of the adversary will transmit deceptive packets in order to divert the sniffer away from the base-station. The simulation results confirm the effectiveness of the approach.

Due to the harsh operation conditions a wireless sensor network (WSN) may suffer large scale damage where many nodes fail simultaneously causing the network to get partitioned into multiple disjoint segments. Restoring network connectivity in such a case is very crucial to avoid negative effects on the application. This paper investigates a relay node (RN) placement strategy to establish inter-segment connectivity and proposes CIST, an algorithm for forming a Connected Inter-Segment Topology. CIST uses segment representation as a means of optimization for minimizing the required number of RNs. The idea behind the algorithm is to find the best subsets of three segments and form a triangular Steiner Minimum Tree with minimum Steiner Points and federate the remaining segment through populating the RNs along mst edges. The performance of CIST is validated through simulation.

In a wireless sensor network nodes probe ambient conditions and send the measurements over multi-hop routes to a base-station. The base-station aggregates the received reports, and based on the findings it orchestrates a response either autonomously or through consultation with a remote commend center. Thus, the role of the base-station is so crucial that the network can be non-functional and/or isolated if the base-station breaks down or gets destroyed. No wonder in a hostile environment, an adversary will target the base-station to inflict the most damage to the network. The fact that the base-station acts as a sink of all data traffic makes it vulnerable to attacks by tracking packet transmission and detecting its location. This paper investigates a novel strategy to counter traffic analysis and boosts the anonymity of the base-station. A sensor transmits at a higher power in order to increase its number of neighbors and confuse an adversary who is assessing the linkability of nodes in quest to identify the route to the base-station. Both analytical and simulation results are provided to capture the effect of increased transmission power on the base-station anonymity.

Localization in underwater environments has been constrained by the dependencies on the line of sight (LOS) due to the challenging variability's of the environment. This dependency hinders node discovery and ad-hoc formation in underwater networks and limits the performance of routing protocols. Most proposed algorithms in the literature rely on anchor nodes that are at fixed positions to serve as reference points, which is not practical in many applications. This paper introduces a novel approach to the localization problem that allows for node discovery without depending on the LOS and any fixed reference nodes. In the proposed surface-based reflection anchor-free localization (SBRAL) algorithm all nodes will apply homomorphic deconvolution to establish a water-surface-reflected communication link. SBRAL then creates a relative coordinate system where every node in the network identifies others nodes by increasing the SBRAL transmit angle and using the reflection points on the water surface as temporary reference points. The simulation results confirm the effectiveness of the proposed SBRAL algorithm.

Multicast streams are the most popular traffic pattern in many applications of mobile ad-hoc networks (MANET). However, the mobility of nodes causes frequent changes to the network topology and thus efficient routing of multicast traffic becomes very challenging. The establishment of a core-mesh has been deemed as a very effective solution for these dynamic setups. The core-mesh acts as a backbone in order to avoid frequent route discovery. However maintaining the core-mesh imposes significant overhead that dominates the performance for large networks. This paper advocates a novel methodology that combines network clustering and mesh-based multicast routing. The Cluster-Mesh based Multicast Routing (CMMR) methodology forms a backbone using cluster-heads. This makes the backbone more stable against frequent node mobility and enables scalability by limiting the scope of the core maintenance. The paper mainly captures the performance advantage of CMMR over contemporary node-based core-mesh strategies. A mathematical model is developed to analyze the average performance of one of the contemporary core-mesh routing schemes and an implementation of the CMMR methodology using the same mesh formation algorithm. The numerical results demonstrate the advantage of CMMR.

In Wireless Sensor-Actor Networks (WSANs), sensors probe their surroundings and send their data to more capable actor nodes in order to execute an application task. The actors' response is mostly collaborative and requires them to coordinate their operation. Therefore, a strongly connected inter-actor topology would be necessary at all time and tolerance of an actor failure becomes a design requirement. Autonomous repositioning of actor nodes has been deemed as an effective recovery strategy. This paper presents a Least-Movement Topology Repair (LeMoToR) algorithm. LeMoToR is a distributed scheme that relies on the local view of a node about the network. To restore connectivity, LeMoToR strives to relocate the least number of nodes and reduce the traveled distance and message complexity. Unlike contemporary schemes that maintain 1 or 2-hop neighbor lists, LeMoToR utilizes existing path discovery activities in the network in order to know the structure of the topology and avoids imposing additional pre-failure communication overhead. The simulation results validate the effectiveness of LeMoToR.

In most applications of Wireless sensor and actor network it is important to sustain connectivity among all actors at all times. When an actor fails the inter-actor topology may get partitions into disjoint blocks and the application may be negatively impacted. Tolerating the actor failure and restoring the lost connectivity need to be performed while imposing the least overhead on the individual actors. In this paper a Least Distance Movement Recovery (LDMR) algorithm is proposed. LDMR is a distributed approach that exploits non cut-vertices actors in the recovery process. The idea is for a set of direct neighbours of the failed node to move toward the position of the failed node while its original position is replaced with the nearest non cut-vertex actor. The recovery process starts with the search phase where each neighbour broadcasts a message containing the failed node ID, neighbour node ID and, Time-To-Live (TTL). When a neighbour receives responses, it chooses the best candidate based on a certain criteria (e.g. distance). We compare our approach with Recovery through Inward Motion (RIM) which depends only on cascaded movements. Extensive simulation experiments are carried out to validate the performance.

In wireless sensor networks, nodes probe ambient conditions in their surrounding and report back to the base-station via multi-hop routing. In a hostile environment the network may be subject to adversary attacks. Given the role that the base-station plays, it can be targeted in order to inflict the most damage to the network. Although stealth design and other physical precautionary measures may be pursued to hide the base-station, the fact that the base-station acts as a sink of all data transmission enables an adversary to employ traffic analysis techniques and identify the location of the base-station. This paper presents a novel approach for countering such traffic analysis and boosting the anonymity of the base-station. Sensors in low activity areas will send out deceptive packets among each other in order to distract the attention of the adversary and make the traffic analysis inconclusive. Simulation results show the effectiveness of the approach.

Wireless Sensor Networks (WSNs) can be deployed to serve mission-critical applications in hostile environments such as battlefield and territorial borders. In these setups, the WSN may be subject to attacks in order to disrupt the network operation. The most effective way for an adversary to do so is by targeting the Base-Station (BS), where the sensor data are collected in the field. By identifying and locating the BS, the adversary can launch attacks to damage or disrupt the operation of the BS. Therefore, maintaining the BS anonymity is of utmost importance in WSNs. While a number of techniques have been proposed in the literature to boost BS's anonymity, no study has exploited the impact of the number of deployed BS and the BS mobility to boost anonymity. This paper fills this gap. We first compare the BS anonymity of one stationary BS to multiple stationary BS under different network topologies. Our results show that having more base-stations can boost both the average and max anonymity of BS nodes. Then we allow some of the base-stations to be mobile, and can move to the lowest anonymity region. Our results show that having one mobile BS can help further increasing the anonymity of the BS; however it is not the case when having more than one BS.

Abstract The limited channel capacity and the varying propagation conditions of radio signals have motivated research for boosting the achievable throughput in wireless networks. Among the effective optimization strategies is to dynamically adjust the packet size either to better suit the channel conditions or to minimize the number of overhead bits in the individual packets. However, multi‐levels of security requirements impose constraints on the data mix in the packet payload and may diminish the gains achievable by contemporary packet‐size optimization schemes. This paper presents a novel bandwidth optimization algorithm for wireless data acquisition networks where strict confidentiality requirements and access restriction policies have to be observed. The algorithm exploits the classification of data in minimizing the number of packet transmissions as well as the overhead within the individual packets. The idea is to combine the transmission of packets based on the time sensitivity and security attributes of the data in the payload. The performance of the proposed algorithm is validated through mathematical analysis and through simulation. The simulation results confirm the effectiveness of the algorithm in boosting data throughput in the network. Copyright © 2010 John Wiley & Sons, Ltd.

In some applications of wireless sensor networks (WSNs), it may be necessary to link a number of disjoint segments in order to form a federated system. The segments can be simply distinct WSNs that operate autonomously or partitions of a single WSN that has suffered significant damage. Linking these segments may be subject to different intersegment quality of service (QoS) requirements. This paper presents an effective approach for federating these segments by populating the least number of relay nodes (RNs) such that the connectivity and QoS requirements are satisfied. Finding the optimal number and position of RNs is NP-hard and heuristics are thus pursued. The deployment area is modeled as a grid with equal-sized cells. A cost is assigned to each cell based on the residual capabilities of relays populated in it. The optimization problem is then mapped to finding the cell-based least-cost paths that collectively meet the QoS requirements. The performance of our approach is validated through extensive simulation experiments. We further demonstrate the beneficial aspects of the resulting topology with respect to degree of connectivity and fault resilience.

Recently, Wireless Sensor and Actor Networks have been receiving a growing attention from the research community because of their suitability for critical applications. Maintaining inter-actor connectivity becomes extremely crucial in such situations where actors have to quickly plan optimal coordinated response to detected events. Failure of critical actor partitions the inter-actor network into disjoint segments, and thus hinders the network operation. Autonomous detection and rapid recovery procedures are highly desirable in such case. This paper presents PCR, a novel distributed partitioning detection and connectivity restoration algorithm. PCR proactively identifies critical actors based on local topological information and designate appropriate backup nodes (preferably non-critical) to handle their failure. A backup actor detects the failure and initiates a recovery process that may involve coordinated multi-actor relocation. The purpose is to avoid procrastination, localize the scope of recovery process and minimize the movement overhead. Simulation results validate the performance of PCR that outperforms contemporary schemes found in literature.

In an underwater environment signal propagation for the acoustic channel is subject to major multipath effect. Therefore, most underwater communication schemes require that the position of the transmitter or receiver is fixed while using directional antennas in order to ensure high signal-to-noise ratio. However, such a requirement hinders node discovery and ad-hoc formation of underwater networks and restraints communication between autonomous underwater vehicles (AUVs) where node locations change over time. This paper proposes a novel approach to underwater communications by relying on the water surface to establish communication links. The proposed surface-based reflection (SBR) model works by requiring the transmitting node to direct its energy towards the water surface. The receiver then applies homomorphic deconvolution techniques to determine the channels impulse response used in obtaining the reflected signal. The receiver is then able to determine the location of the transmitter by triangulating the transmitted and reflected signals with respect to the water surface. Simulation experiments are provided to validate the SBR approach.

Wireless sensor nodes are symbiotic when deployed in an activity region and heavily rely on each other for successful transmission of data. Therefore, failure of some nodes can possibly partition the network. Since these networks often operate unattended, nodes need to collectively maintain connectivity and resolve any reachability problem. Most published approaches for restoring connectivity are based on a single underlying principle of replacing the failed node without considering the possible fact that the location of the failed node could have been the reason for its failure. These approaches also tend to trigger a cascaded relocation of many nodes resulting in increased overhead. This paper presents a novel solution that pursues rearrangement of nodes while limiting the scope of the recovery to the vicinity of the failed node. The connectivity restoration is modeled as a variant of the Steiner tree formation problem and solved using novel heuristics. The proposed approach is validated through simulation.

In Wireless Sensor-Actor Networks (WSANs), actors collect sensor readings and respond collaboratively to achieve an application mission. Since actors coordinate their operation, a strongly connected network topology would be required at all time. In addition, the path between actors may have to be capped in order to meet latency constraints. However, a failure of an actor may cause the network to partition into disjoint blocks and would thus violate such connectivity goal. One of the effective recovery methodologies is to autonomously reposition a subset of the actor nodes to restore connectivity. Contemporary schemes rely on maintaining 1 or 2-hop neighbor lists and predetermine criteria for node's involvement in the recovery. However, 1-hop based schemes often impose high node relocation overhead. In addition, the repaired inter-actor topology using 2-hop schemes often differs significantly from its pre-failure status and some inter-actor data paths may get extended. This paper presents a Least-Disruptive topology Repair (LeDiR) algorithm. LeDiR relies on the local view of a node about the network in order to devise a recovery plan that relocates the least number of nodes and ensures that no path between any pair of nodes is extended. LeDiR is a localized and distributed algorithm that leverages existing path discovery activities and imposes no additional pre-failure communication overhead. LeDiR is validated through simulation and is shown to outperform existing schemes.

Sensor networks have gained importance in tracking of vital information. The accuracy of the sensor readings plays a major role in crucial applications like military and defense operations, and environment monitoring where the error margin has to be extremely small. In addition, sensor nodes are typically constrained in their computation, communication, and energy capacities and there is usually a need for managing sensor activities to utilize these limited resources in a judicious manner. Therefore, a participation criterion for the sensor nodes to balance fidelity of the collected data and conservation of nodes resources is important. This paper presents a novel node engagement strategy. The idea is to convoy a subset of the collected data reports while ensuring consensus of the nodes. Basically, nodes rotate transmission duties among neighbors. The data sent to the base-station will be based on the readings of the designated sensor. The neighboring nodes will act as passive listeners and stay quiet if the data does not deviate significantly from their measurements, which implies their endorsement of the accuracy of what the base-station will get. Otherwise, a subset of these nodes comes forward and transmits their readings so that the base-station can aggregate and accurately estimate the true value. Simulation results confirm the effectiveness of the proposed approach in terms of energy conservation and responsiveness to drops in data fidelity.

Wireless sensor networks (WSN) often operate unattended in harsh and inhospitable environments. While such deployment eliminates/reduces human intervention and provided fully-automated data gathering systems, WSNs are prone to sensors failure which not only can degrade the quality of coverage but also disrupt the data traffic. To address such a problem, most approaches in the literature deploy redundant nodes during network setup and reconfigure the network topology to establish alternate data paths. However, sometimes the network suffers a loss of a critical node or a large scale damage that involves many nodes and would thus create multiple disjoint partitions. For these cases, a provisioned approach for tolerating occasional failures at the network design level will not be effective. This talk analyzes the effect of a node failure on connectivity and explores the different recovery options. A number of schemes for connectivity restoration will be described. A summary of ongoing efforts and open research problems will be also presented.

Federating disjoint segments may be necessary in some applications of wireless sensor networks (WSNs). The segments can be simply distinct WSNs that operate autonomously or partitions of a single WSN that has suffered a significant damage. Linking these segments is subject to varying distances among segments which may be longer than twice the communication range of a relay node. In this work, we focus on designing an effective approach for federating these segments by populating the least number of relay nodes. The deployment area is modeled as a grid with equal-sized cells. The optimization problem is then mapped to selecting the fewest count of cells to populate relay nodes such that all segments are connected. Finding the optimal number and positions of relay nodes with respect to length between segments is NP-hard and heuristics are thus pursued. We propose a distributed Cell-based Optimized Relay node Placement (CORP) algorithm and explain the beneficial aspects of the resulting topology with respect to connectivity, and traffic balance. The performance of CORP is validated through extensive simulation experiments.

Recent years have witnessed a growing interest in the applications of wireless sensor networks (WSNs). In some of these applications, such as search and rescue and battlefield reconnaissance, a set of mobile nodes is deployed in order to collectively survey an area of interest and/or perform specific surveillance tasks. Such collaboration among the sensors requires internode interaction and thus maintaining network connectivity is critical to the effectiveness of WSNs. While connectivity can be provisioned at startup time and then sustained through careful coordination when nodes move, a sudden failure of a node poses a challenge since the network may get partitioned. This paper presents RIM; a distributed algorithm for Recovery through Inward Motion. RIM strives to efficiently restore the network connectivity after a node failure. Instead of performing a networkwide analysis to assess the impact of the node failure and orchestrate a course of action, RIM triggers a local recovery process by relocating the neighbors of the lost node. In addition to minimizing the messaging overhead, RIM opts to reduce the distance that the individual nodes have to travel during the recovery. The correctness of the RIM algorithm is proven and the incurred overhead is analyzed. The performance of RIM is validated through simulation experiments.

A wide range of applications such as disaster management, military, and security have fueled interest in sensor networks in recent years. Since the employed sensor nodes often are significantly constrained in their onboard energy, most of the research work, including that on medium access control (MAC) protocols, has focused on optimizing energy consumptions and cared less about data delivery latency. However, employing advanced video and audio sensing devices needs special attention to quality of service requirements associated with multimedia data. This paper presents MAQ, a novel Medium Arbitration scheme for supporting QoS traffic over a single communication channel. MAQ decentralizes the control of the communication resources and fully integrates TDMA and CSMA/CA schemes for medium access. The main objective of MAQ is to ensure predictable delay and fair access for the nodes while achieving high channel utilization and network throughput. The simulation experiments confirm the effectiveness of MAQ.

Multicast streams are the dominant application traffic pattern in many mission critical ad-hoc networks. The limited computation and communication resources, the large scale deployment and the unguaranteed connectivity to trusted authorities make known security solutions for wired and single-hop wireless networks inappropriate for such application environment. This paper promotes a novel Tiered Authentication scheme for Multicast traffic (TAM) for large scale dense ad-hoc networks. Nodes are grouped into clusters. Multicast traffic within the same cluster employs one-way chains in order to authenticate the message source. Cross-cluster multicast traffic includes a message authentication codes (MACs) that are based on a set of keys. Each cluster uses a unique subset of keys to look for its distinct combination of valid MACs in the message in order to authenticate the source. TAM thus combines the advantages of the secret information asymmetry and the time asymmetry paradigms and exploits network clustering to reduce overhead and ensure scalability. The numerical and analytical results demonstrate the performance advantage of TAM.

Wireless sensor networks (WSN) are becoming an attractive choice for many critical applications, such as border protection and combat field reconnaissance. In these applications, sensors probe their surroundings and send their findings to a base-station (BS) over multi-hop paths. Given the important role of the BS, an adversary who likes to disrupt the network operation would eagerly look for where the BS could be and target it with attacks in order to inflict maximum damage. The continuous flow of traffic towards the BS creates a pronounced pattern of wireless links that may expose the BS position and thus make the network more vulnerable. This paper investigates means for boosting the anonymity of the BS. First, we adapt three models-entropy based model, GSAT test and evidence theory model, to quantify anonymity in the context of WSN. We further customize models that conventionally measure anonymity of the entire network, to suit the BS. Then, a novel approach for boosting the anonymity of the BS is proposed. The idea is for the BS to disguise itself by transmitting some of the data packets it receives with varying intensity. The goal is to create a perception that the BS node is just another sensor node sending data and thus confuse the adversary. The approach is validated through simulation.

In some applications of wireless sensor networks (WSNs) it may be necessary to federate a number of disjoint segments. Linking these segments may be subject to varying inter-segment quality of service (QoS) requirements. This paper presents an effective approach for federating these segments. The main idea is to place relay nodes (RNs) in order to establish intersegment connectivity with the least number of RNs while meeting the desired QoS requirements. Finding the optimal number and position of RNs is shown to be NP-hard and heuristics are thus pursued. The deployment area is modeled as a grid with equal-sized cells. Each cell is evaluated based on the residual capabilities of RNs populated in the cell. The optimization problem is then mapped to finding the cell-based least cost paths that collectively meet the QoS requirements. The performance of the proposed approach is validated through simulation.

Mobile sensor networks rely heavily on inter-sensor connectivity for collection of data. Nodes in these networks monitor different regions of an area of interest and collectively present a global overview of some monitored activities or phenomena. Since failure of a sensor leads to loss of connectivity it may cause a partitioning of the network. A number of approaches have been recently proposed that pursue node relocation in order to restore connectivity. However, these approaches tend to ignore the possible loss of coverage in some areas, either due to the failure itself or due to the connectivity- limited focus of the recovery. This paper opts to address the connectivity and coverage concerns in an integrated manner. A novel Coverage Conscious Connectivity Restoration (C3R) algorithm is presented. C3R involves one or multiple neighbors of the failed node to recover from the failure. Each neighbor temporarily relocates to substitute the failed node, one at a time, and then returns back to its original location. This leads to intermittent connectivity and monitoring of all the originally covered spots. C3R is validated through simulation. The simulation results confirm the effectiveness of the approach.

In Wireless Sensor and Actor Networks (WSANs) a connected interactor topology is desirable in order for the deployed actors to work collaboratively. If a critical actor fails causing the inter-actor network to get partitioned into disjoint segments, the other actors close to the faulty node often exploit their mobility to autonomously restore the lost inter-actor connectivity. However, such a solution focuses on resource efficiency and assumes no constraints on the mobility of actors which can be impractical in the real scenarios. In addition, since actors need to carry out tasks to meet the application level requirements, unconstrained movement of actor(s) to restore interactor connectivity can cause a major failure at the application level. This paper presents C2AM; a recovery algorithm that factors in application level constraints on actor's mobility while restoring the network connectivity. In addition to considering physical level requirements, C2AM accounts for application level concerns as well in order to avoid major disruptions to ongoing missions. Simulation results have validated the effectiveness of the algorithm in maintaining both objectives.

Wireless sensor networks (WSN) can increase the efficiency of many real-life applications through the collaboration of thousands of miniaturized sensors which can be deployed unattended in inhospitable environments. Due to the harsh surroundings and violent nature of the applications, the network sometimes suffers a large scale damage that involves many nodes and would thus create multiple disjoint partitions. This paper investigates a strategy for recovering from such damage through the placement of relay nodes and promotes a novel approach. The proposed approach opts to re-establish connectivity using the least number of relays while ensuring certain quality in the formed topology. Unlike contemporary schemes that form a minimum spanning tree among the isolated segments, the proposed approach establishes a topology that resembles a spider web, for which the segments are situated at the perimeter. Such a topology not only exhibits stronger connectivity than a minimum spanning tree but also achieves better sensor coverage and enables balanced distribution of traffic load among the employed relays. The simulation results demonstrate the effectiveness of the proposed recovery algorithm.

Welcome to the 34thIEEE Conference on Local Computer Networks (LCN). It has been our privilege to coordinate the review process and put together a high quality technical program. This year LCN received 188 regular full-length (up to 8 pages) and 24 short (up to 4 pages) submissions. Every submitted paper received at least three reviews. Most papers have been evaluated by 4 TPC members.

This paper introduces a novel optimization strategy for increasing the resource efficiency and security of data routing in a wireless sensor network. Most secure routing schemes found in the literature provision security at the highest level by involving sophisticated data encryption with a large key. This imposes lots of overhead both in computation, while performing the encryption and decryption, and in communication due to bit padding. The proposed optimization approach strives to adapt the security measures to the trust level on the nodes on the path and factors in the security-related overhead in the link cost. In addition, a novel path rotation scheme is proposed to enable the use of resource-efficient security measures without risking an increase in vulnerability. The applicability of the proposed optimization is validated using the ad hoc on-demand multipath distance vector (AOMDV) routing protocol and its performance advantage is confirmed via ns2 simulation experiments.

Abstract Steganography is the science and art of avoiding the arousal of suspicion in covert communications. This paper presentsChessSteganography (Chestega), a novel methodology that exploits popular games like chess. Chestega conceals messages in chess related covers such as training documents, game analysis, news articles, etc. Unlike contemporary approaches, Chestega does not exploit noise to embed a message nor produce a detectable noise. Instead, authenticated data can be employed in the cover which makes it resilient to comparison attacks. Chestega is also a public approach that neither relies on the secrecy of its technique, nor need to employ a stega‐key. The paper demonstrates the feasibility of employing authenticated Chess Cover that is generated by Chessmaster 8000. Chestega is further validated through steganalysis. Copyright © 2009 John Wiley & Sons, Ltd.

Recent years have witnessed a growing interest in applications of wireless sensor and actor networks (WSANs). In these applications, a set of mobile actor nodes are deployed in addition to sensors in order to collect sensors' data and perform specific tasks in response to detected events/objects. In most scenarios, actors have to respond collectively, which requires interactor coordination. Therefore, maintaining a connected interactor network is critical to the effectiveness of WSANs. However, WSANs often operate unattended in harsh environments where actors can easily fail or get damaged. An actor failure may lead to partitioning the interactor network and thus hinder the fulfillment of the application requirements. In this paper, we present DARA, a distributed actor recovery algorithm, which opts to efficiently restore the connectivity of the interactor network that has been affected by the failure of an actor. Two variants of the algorithm are developed to address 1- and 2-connectivity requirements. The idea is to identify the least set of actors that should be repositioned in order to reestablish a particular level of connectivity. DARA strives to localize the scope of the recovery process and minimize the movement overhead imposed on the involved actors. The effectiveness of DARA is validated through simulation experiments.

Clustering is a standard approach for achieving efficient and scalable performance in wireless sensor networks. Traditionally, clustering algorithms aim at generating a number of disjoint clusters that satisfy some criteria. In this paper, we formulate a novel clustering problem that aims at generating overlapping multihop clusters. Overlapping clusters are useful in many sensor network applications, including intercluster routing, node localization, and time synchronization protocols. We also propose a randomized, distributed multihop clustering algorithm (KOCA) for solving the overlapping clustering problem. KOCA aims at generating connected overlapping clusters that cover the entire sensor network with a specific average overlapping degree. Through analysis and simulation experiments, we show how to select the different values of the parameters to achieve the clustering process objectives. Moreover, the results show that KOCA produces approximately equal-sized clusters, which allow distributing the load evenly over different clusters. In addition, KOCA is scalable; the clustering formation terminates in a constant time regardless of the network size.

The effectiveness of wireless sensor networks (WSNs) deployed in search and rescue, battlefield reconnaissance, surveillance, and other applications depends on inter-node interaction and maintaining network connectivity. While connectivity can be provisioned at startup, then sustained through careful coordination when nodes move, the network can be partitioned if a node suddenly fails. This paper presents recovery through inward motion (RIM), a distributed algorithm to efficiently restore network connectivity after a node failure. Instead of performing a network-wide analysis to assess the impact of the node failure and set a course of action, RIM triggers a local recovery process by relocating the neighbors of the lost node. RIM minimizes messaging overhead and reduces the distance that individual nodes travel during the recovery. Simulations validate RIM's performance.

The deployment of wireless sensor networks (WSNs) is expected to have a significant impact on the efficiency of many civil and military applications, such as disaster management, environment monitoring, combat field surveillance and space exploration. In unattended WSN setups, most of the energy aware routing approaches pursue multi-hop paths in order to minimize the total transmission power. However, the hops close to the base-station (also known as the sink) become heavily involved in packets receiving and forwarding and thus their batteries get depleted rather quickly, and sometimes become traffic bottlenecks. The failure of these nodes also can create a void around the sink and significantly increase the energy consumed in communication with the sink. This paper evaluates the effectiveness of three approaches for handling the weakened network topology caused by the failure of sensors around the sink. The approaches are: (1) increasing the population of sensors in selected areas; (2) increasing the number of sink nodes in the network; and (3) repositioning the existing sink. The performance of the three approaches is validated in a simulated environment.

Wireless sensor and actor networks (WSANs) employ powerful and mobile actor nodes that can perform application specific actions based on the received data from the sensors. As most of these actions are performed collaboratively among the actors, inter-actor connectivity is one of the desirable features of WSANs. In this paper, we propose a novel distributed algorithm for establishing a connected inter-actor network topology. Considering an initially partitioned actor network with intra-connected sub-networks, our algorithm pursues a coordinated actor movement in order to connect the sub-networks. The goal of this movement is to both minimize the total and maximum travel distances of the individual actors. Our algorithm considers the minimum connected dominating set of each sub-network when picking the appropriate actor to move so that the connectivity of each sub-network is not violated. We analytically study the performance of our algorithm. Extensive simulation experiments validate the analytical results and confirm the effectiveness of our approach.

Wireless sensor networks (WSNs) have recently gained increased attention due to their potential use in numerous civil and military applications. In many of these applications, resource-constrained sensors are deployed to probe their surroundings and report their findings to one or multiple gateway nodes. In large networks, many sensors get involved in data relaying making the data latency unacceptably high. Since the number of deployed gateways and their locations impact the operation of the network, one of the option for reducing the data collection delay is to employ many gateways and appropriately place them in close proximity to data sources. However, increasing the gateway count imposes high cost for the additional hardware resources and their deployment means, thus a tradeoff would be unavoidable. In this paper, we introduce a novel approach for estimating the least number of gateways required for meeting some upper bound on data latency. In addition, we present a heuristic for finding best locations for these gateways. Validation results confirm the effectiveness of the proposed approach.

Wireless sensor networks can be employed in a wide range of applications such as disaster management, combat field surveillance and homeland security. Sensors in such applications are deployed in mass and are usually constrained in the amount of available onboard energy. This paper presents ARCH, a distributed medium access arbitration of multi-radio-channel based sensor networks. ARCH is an energy efficient, scalable and collision free MAC layer protocol that combines frequency and time division principles for medium sharing. To achieve scalability sensors are partitioned into cells. One of the sensors in a cell is designated as a cell-head. ARCH employs a distributed algorithm for arbitrating channels among cells to enable simultaneous non-interfering data collection. Intra-cell transmissions are scheduled by the cell-head using the assigned channel. Cell-heads aggregate the gathered data and forward it over inter-cell-head paths to the base-station. The base-station assigns distinct channels to the independent branches of the inter- cell routing tree. Data transmission and reception on a branch is further scheduled in depth-first ordering. ARCH allows nodes to stay in the sleep mode for the longest duration and avoids collisions and thus it minimizes energy consumption and boosts the robustness of the network operation. The performance of ARCH is validated through simulation.

Many applications of wireless sensor networks (WSNs) have emerged over the last few years. In such networks resource-constrained sensor nodes are deployed to probe their surroundings and send the collected data to one or multiple gateways for processing. Therefore, proper placement of sensors and gateways in the area of interest facilities communications and allows for better operation of the network. While sensors in many WSN applications are randomly deployed, gateways often can be placed in a controlled manner. In this paper, we introduce two novel algorithms for proper positioning of gateways in WSNs. We employ genetic algorithms for selecting the best spot for placing each gateway so that sensors' data can be delivered to a gateway with the least latency. The two algorithms differ in their complexity and the quality of the solution that they can achieve and thus enable a design level trade-off. Validation results confirm the effectiveness of both algorithms in reducing the data delivery delay and their positive impact on other performance metrics such as the network lifetime.

In addition to the miniaturized sensor nodes, wireless sensor and actor networks (WSANs) employ significantly more capable actor nodes that can perform application specific actions to deal with events detected and reported by the sensors. Since these actions can be taken at any spot within the monitored area, the actors should be carefully placed in order to provide maximal coverage. Moreover, the actors often coordinate among themselves in order to arbitrate tasks and thus inter-actor connectivity is usually a requirement. In this paper, we propose a distributed actor positioning algorithm that maximizes the coverage of actors without violating the connectivity requirement. The approach applies repelling forces between neighboring actors, similar to molecular particles in Physics, in order to spread them in the region. However, the movement of each actor is restricted in order to maintain the connectivity of the inter-actor network. The performance of the approach is validated through simulations.

Knowledge of node's locations is an essential requirement for many applications of wireless sensor networks. A major problem with multi-hop location discovery is the accumulated error. In this paper, we analyze the effect of reflection errors and present the Multi-hop Relative Location Estimation (MRLE) algorithm that estimates relative node's positions with low error margins. We capture the impact of different parameters on the accuracy of the estimated position and introduce a new metric, called the CLIQUE factor, that has a very dominant effect on the accuracy of the estimated positions. We further highlight means for trading accuracy for energy consumption and/or computational overhead.

There has been an increased interest in applications of wireless sensor and actor networks (WSANs) in recent years. In such applications, a set of mobile actor nodes are deployed in addition to sensors in order to collect sensors' data and perform specific tasks in response to detected events/objects. In most scenarios actors have to respond collectively which requires an inter-actor coordination. Therefore, maintaining a connected inter-actor network is crucial to the effectiveness of WSANs. However, WSANs often operate unattended in harsh environments where actors can easily fail or get damaged. Due to such failures an actor will be unable to communicate with its neighbors which may lead to partitioning the inter-actor network. In this paper we present DARA; a Distributed Actor Recovery Algorithm, which opts to efficiently restore the connectivity of the inter-actor network that has been affected by the failure of an actor. The idea is to identify the least set of actors that should be repositioned in order to establish connectivity among disjoint network partitions. DARA strives to localize the scope of the recovery process and minimize the movement overhead imposed on the involved actors. The effectiveness of DARA is validated through simulation experiments.

Wireless sensor networks comprise individual sensor nodes with limited storage and computational power. These restrictions can cause difficulty when establishing secure communication links between nodes. Expensive encryption schemes, such as Diffie-Hellman key exchange, simply cannot be effectively implemented. As a consequence, alternative methods are necessary to provide adequate security. One such technique involves randomly distributing several encryption keys and then requiring nodes to discover neighbors that share a subset of these assigned keys. Once compatible neighbors are located, communication is secured through the use of symmetric encryption. Though the requirements for this scheme are less than complex asymmetrical algorithms, each sensor node is still required to store several keys and node-to-key associations. In this paper, we advocate a simplistic approach that reduces key storage while limiting a network's susceptibility to being compromised. The approach is based on assigning a single randomly-selected key to each node. The paper presents a detailed analysis of the resilience of the proposed scheme to the collusion attacks and studies the implications on the network connectivity.

Abstract Recent technological advances in mobile computing and wireless communication have made portable devices, such as PDA, laptops, and wireless modems to be very compact and affordable. On the other hand, wireless networks have gained such wide popularity that new network infrastructure is continually introduced. It is thus likely that many of the future portable devices will be equipped with multiple wireless modems such as Bluetooth and 802.11 WLAN, in order to increase device inter‐operability. The availability of multiple modems can leverage the performance of the communication traffic generated by the applications, for example Internet access. We envision a tool for managing the device connection through these modems. At the core of this tool is an optimization engine that splits packet traffic across a subset of the available transports so that user's performance metrics are maximized. This paper describes a mathematical model for such an optimization problem considering its applicability to small portable devices. Relevant quality of service (QoS) parameters such as bandwidth, average delay, and energy consumption are covered in the model. The mathematical formulation is validated using a simulated environment. The experimental results have demonstrated the effectiveness of our model and captured the inter‐relationship among the quality parameters. Copyright © 2006 John Wiley & Sons, Ltd.

Clustering is a standard approach for achieving efficient and scalable performance in wireless sensor networks. Most of the published clustering algorithms strive to generate the minimum number of disjoint clusters. However, we argue that guaranteeing some degree of overlap among clusters can facilitate many applications, like inter-cluster routing, topology discovery and node localization, recovery from cluster head failure, etc. We formulate the overlapping multi-hop clustering problem as an extension to the k-dominating set problem. Then we propose MOCA; a randomized distributed multi-hop clustering algorithm for organizing the sensors into overlapping clusters. We validate MOCA in a simulated environment and analyze the effect of different parameters, e.g. node density and network connectivity, on its performance. The simulation results demonstrate that MOCA is scalable, introduces low overhead and produces approximately equal-sized clusters.

Recently, wireless sensor networks (WSN) have received enormous attentions due to their potential use in many applications. They can be used to enrich our understanding of natural events, such as earthquakes and volcanoes, and to increase the efficiency of surveillance operations in secure installation, border control and military reconnaissance. Sensors are placed in harsh environments to collect and deliver data to a central node, called the gateway. The gateway analyzes the received data and decides on appropriate actions. Therefore, protecting the gateway is critical for ensuring the robustness of WSN. Since the location of the gateway significantly affects the efficiency of the network operation, many research have been conducted for the gateway placement problem. However, most of the proposed solutions are geared for boosting network-related performance metrics, such as throughput and energy consumption. We argue that relocating without taking safety concerns into consideration may cause the gateway to move dangerously close to one or multiple serious events in the environment. In this paper, we present GRENN, a new algorithm for gateway relocation in wireless sensor networks considering both the network performance and the gateway safety. Our experimental validation has demonstrated the effectiveness of GRENN in protecting the gateway while keeping the performance at an acceptable level.

In wireless sensor networks (WSNs), large populations of tiny sensor nodes probe the environment and report the collected data to a base-station(s) for processing. Due to the resource constraints, WSNs pose numerous and non-conventional design challenges compared to contemporary communication networks. One of the performance optimization techniques that have been recently proposed is to dynamically relocate the base-station closer to the sensors involved in heavy packet traffic in order to increase energy efficiency, packet delivery ratio and timeliness. In this paper, we argue that such relocation may jeopardize the base-station since it often moves the base-station too close to danger zones. We present STEER, an efficient safety-oriented relocation algorithm for base-stations that strives to protect the base-station while maintaining good network performance. The effectiveness of our algorithm is validated in a simulated environment.

In most applications of wireless sensor networks (WSNs), sensors are constrained in the amount of available energy and computation resources. Unlike traditional networks, protocols for sensor networks have to be designed with these constraints in mind. This paper proposes ARM, an ARchitectural and Management schemes for efficient data collection in WSNs. ARM employs a suite of distributed algorithms and pursues an integrated approach to network management. The sensor network is divided into cells to achieve scalability. The cells use a novel distributed medium arbitration scheme, which enables simultaneous collision-free data transmission. The predictable access allows nodes to stay in the sleep mode for the longest possible time and thus minimizes energy consumption

In addition to the sensors, wireless sensor and actor networks (WSANs) employ significantly more capable actor nodes that can perform application specific actions. In these setups responsiveness to serious events is of utmost importance and thus requires minimal latency in both data gathering and action completion. In addition, since these actions are often taken at or close to where events are detected, which can be any spot within the monitored area, the actors should strive to provide maximal coverage of the area. In this paper, we propose COLA, an actor placement mechanism that considers both the delay requirements of data collection and the coverage. COLA first evenly distributes the actors in the region for maximized coverage. Actors then collaboratively partition the sensors, forming clusters. Each individual actor then repositions itself at a location that enables minimal latency in collecting data. The effectiveness of COLA is evaluated by extensive simulations.

Recent advances in wireless sensor networks (WSNs) are fueling the interest in their application in a wide variety of sensitive settings such as battlefield surveillance, border control, and infrastructure protection. Data confidentiality and authenticity are critical in these settings. However, the wireless connectivity, the absence of physical protection, the close interaction between WSNs and their physical environment, and the unattended deployment of WSNs make them highly vulnerable to node capture as well as a wide range of network-level attacks. Moreover, the constrained energy, memory, and computational capabilities of the employed sensor nodes limit the adoption of security solutions designed for wire-line and wireless networks. In this paper, we focus on the management of encryption keys in large-scale clustered WSNs. We propose a novel distributed key management scheme based on exclusion basis systems (EBS); a combinatorial formulation of the group key management problem. Our scheme is termed SHELL because it is scalable, hierarchical, efficient, location-aware, and light-weight. Unlike most existing key management schemes for WSNs, SHELL supports rekeying and, thus, enhances network security and survivability against node capture. SHELL distributes key management functionality among multiple nodes and minimizes the memory and energy consumption through trading off the number of keys and rekeying messages. In addition, SHELL employs a novel key assignment scheme that reduces the potential of collusion among compromised sensor nodes by factoring the geographic location of nodes in key assignment. Simulation results demonstrate that SHELL significantly boosts the network resilience to attacks while conservatively consuming nodes' resources

Abstract: Recent years have witnessed a growing interest in the application of wireless sensor networks in unattended environments. Nodes in such applications are equipped with limited energy supply and need careful management in order to extend their lifetime. In order to conserve energy, many of the routing protocols proposed for wireless sensor networks reduce the number of transmitted packets by pursuing in-network data aggregation. Almost all of the aggregation schemes presented in the literature strive to save sensor’s energy while considering unconstrained data traffic. However, aggregation extends the queuing delay at the relay nodes and can thus complicate the handling of latencyconstrained data. In this paper, we analyze the conditions for effective aggregation of data traffic that is subject to end-to-end delay constraints. We present an algorithm for achieving maximal possible energy saving through data aggregation while meeting the desired level of timeliness. A Weighted Fair Queuing based mechanism for packet scheduling is employed at each node in order to perform service differentiation and ensure bounded delay for constrained traffic. The performance of the proposed approach is qualified via simulation. 1.

Due to the dynamic nature of wireless ad-hoc networks (WAHNs) and the multi-node involvement in most WAHN applications, group key management has been proposed for efficient support of secure communications. Exclusion basis systems (EBS) provide a framework for scalable and efficient group key management where the number of keys per node and the number of re-key messages can be relatively adjusted. EBS-based solutions, however, may suffer from collusion attacks, where a number of nodes may collaborate to reveal all system keys and consequently capture the network. In this paper we investigate the collusion problem in EBS and demonstrate that a careful assignment of keys to nodes reduces collusion. Since an optimal assignment is NP hard, we propose a location-based heuristic where keys are assigned to neighboring nodes depending on the Hamming distance between the strings of bits representing the used subset of the keys.

There has been a growing interest in the applications of wireless sensor networks in unattended environments. In such applications, sensor nodes are usually deployed randomly in an area of interest. Knowledge of accurate node location is essential in order to correlate the gathered data to the origin of the sensed phenomena and assure the relevance of the reported information. In this paper, we present an efficient anchor-free protocol for localization in wireless sensor networks. Each node discovers its neighbors that are within its transmission range and estimates their ranges. Our algorithm fuses local range measurements in order to form a network wide unified coordinate systems while minimizing the overhead incurred at the deployed sensors. Scalability is achieved through grouping sensors into clusters. Simulation results show that the proposed protocol achieves precise localization of sensors and maintains consistent error margins.

Wide range of applications such as disaster management, military and security have fueled the interest in sensor networks during the past few years. Sensors are typically capable of wireless communication and are significantly constrained in the amount of available resources such as energy, storage and computation. Such constraints make the design and operation of sensor networks considerably different from contemporary wireless networks, and necessitate the development of resource conscious protocols and management techniques. In this paper, we present an energy-efficient, scalable and collision-free MAC layer protocol for sensor networks. The approach promotes time-based arbitration of medium access to limit signal interference among the transmission of sensors. Transmission and reception time slots are prescheduled to allow sensors to turn their radio circuitry off when not engaged. In addition, energy consumption due to active to sleep mode transitions is minimized through the assignment of contiguous transmission/reception slots to each sensor. Scalability of the approach is supported through grouping of sensors into clusters. We describe an optimization algorithm for energy conscious scheduling of time slots that prevents intra-cluster collisions and eliminates packet drop due to buffer size limitations. In addition, we also propose an arbitration scheme that prevents collisions among the transmission of sensors in different clusters. The impact of our approach on the network performance is qualified through simulation.. Copyright © 2004 John Wiley & Sons, Ltd.

In recent years, due to increasing interest in applications of wireless sensor networks that demand certain quality of service (QoS) guarantees, new routing protocols have been proposed for providing energy-efficient real-time relaying of data. However, none of these protocols considered any possible movement of the sink node for performance purposes. In this paper, we propose possible relocation of sink (gateway) for improving the timeliness of real-time packets. Our approach searches for a location close to the most loaded node. The gateway is then relocated to the new location so that the load of that node is alleviated and the real-time traffic can be split. As long as the gateway stays within the transmission range of all last hop nodes, it can be moved to that location without affecting the current route setup. Otherwise routes are adjusted by introducing new forwarders. Simulation results demonstrate the effectiveness of the proposed approach.

Given the growing number of applications of wireless sensor networks, some recent research has focused on managing secure wireless communications in such networks. When a large group of sensors that typically are constrained in energy, computation and communication resource are deployed, efficient key management becomes critical. In this paper, we propose a new hierarchical key management scheme for wireless sensor networks based on a combinatorial optimization of the group key management problem. Our solution uses symmetric encryption and re-keying to support current, forward and backward secrecy. An important contribution of our solution is that it yields optimal results for the number of administrative keys per network granule and the number of re-key messages. We also present a mechanism for recovery from a node failure or compromise.

Wireless sensor networks have potential to monitor environments for both military and civil applications. Due to inhospitable conditions these sensors are not always deployed uniformly ion the area of interest. Since sensors are generally constrained in on-board energy supply, efficient management of the network is crucial to extend the life of the sensors. Sensors' energy cannot support long haul communication to reach a remote command site and thus requires many levels of hops or a gateway to forward the data on behalf of the sensor. In this paper, we propose an algorithm to network these sensors in to well define clusters with less energy-constrained gateway nodes acting as cluster-heads, and balance load among these gateways. Simulation results show how our approach can balance the load and improve the lifetime of the system.

Wireless sensor networks have a wide spectrum of civil and military applications that call for security, e.g., target surveillance in hostile environments. Typical sensors possess limited computation, energy, and memory resources; therefore the use of vastly resource-consuming security mechanisms is not possible. In this paper, we propose a cryptographic key management protocol, which is based on the IBSK scheme, but only two symmetric keys are required to be pre-deployed at each sensor. The protocol supports the eviction of the compromised nodes. Simulation shows that the key management is remarkably low thanks to the multi-tier network architecture in which only sensor-to-gateway secure sessions are allowed, and reports order-of-magnitude improvement in energy saving as compared to the original IBSK scheme, and Kerberos-like schemes.

Sensor networks have recently gained a lot of attention from the research community. Sensors are significantly resource-constrained devices and last till the depletion of their batteries. Sensor networks typically have a large number of nodes. To ensure scalability sensor networks are often partitioned into clusters, each managed by a cluster head (gateway). Efficient management of a sensor network for extending the lifetime of the network is among the prominent areas of research in this domain. While most of the previous research focused on the optimal use of sensor's energy, very little attention has been paid to the efficiency of energy usage at the gateway. Tasks need to be allocated to gateways in such a way that maximizes the life of these cluster-heads and eventually the whole network. In this paper, we present an optimization scheme for task allocation to gateways. The task allocation problem is modeled as a zero-one nonlinear program. Simulation results show that substantial energy savings can be obtained with the proposed method.

During the past few years distributed wireless sensor networks have been the focus of considerable research for both military and civil applications. Sensors are generally constrained in on-board energy supply therefore efficient management of the network is crucial to extend the life of the system. Sensors' energy cannot support long haul communication to reach a remote command site, thus they require multi-tier architecture to forward data. An efficient way to enhance the lifetime of the system is to partition the network into distinct clusters with a high-energy node called a gateway as cluster-head. Failures are inevitable in sensor networks due to the inhospitable environment and unattended deployment. However, failures in higher level of hierarchy e.g. cluster-head cause more damage to the system because they also limit accessibility to the nodes that are under their supervision. In this paper we propose an efficient mechanism to recover sensors from a failed cluster. Our approach avoids a full-scale re-clustering and does not require deployment of redundant gateways.

While traditional routing protocols try to minimize the end-to-end delay or maximize the throughput, most energy-aware routing protocols for wireless sensor networks try to extend the life time of the network by minimizing the energy consumption sacrificing other performance metrics. We introduce a new energy-aware routing protocol that tries to minimize the energy consumption and, at the same time, maintain good end-to-end delay and throughput performance. The new algorithm is based on a constrained shortest-path algorithm. We compare the new algorithm with some traditional routing and energy-aware routing algorithms. The results show that the new algorithm performance is acceptable under all performance metrics and presents a performance balance between the traditional routing algorithms and the energy-aware routing algorithms. The constraint value can be chosen to achieve different performance objectives for different sensor network missions.

In the recent development of avionics systems, integrated modular avionics (IMA) is advocated for next generation architecture that needs integration of mixed criticality real-time applications. These integrated applications meet their own timing constraints while sharing avionics computer resources. To guarantee timing constraints and dependability of each application, an IMA-based system is equipped with the schemes for spatial and temporal partitioning. We refer the model as SP-RTS (strongly partitioned real-time system), which deals with processor partitions and communication channels as its basic scheduling entities. This paper presents a partition and channel-scheduling algorithm for the SP-RTS. The basic idea of the algorithm is to use a two-level hierarchical schedule that activates partitions (or channels) following a distance-constraints guaranteed cyclic schedule and then dispatches tasks (or messages) according to a fixed priority schedule. To enhance schedulability, we devised heuristic algorithms for deadline decomposition and channel combining. The simulation results show the schedulability analysis of the two-level scheduling algorithm and the beneficial characteristics of the proposed deadline decomposition and channel combining algorithms.

Engineering of complex distributed real-time applications is one of the hardest tasks faced by the software profession today. All aspects of the process, from design to implementation, are made more difficult by the interaction of behavioral and platform constraints. Providing tools for this task is likewise not without major challenges. In this paper, we discuss a tool suite which supports the development of complex distributed real-time applications in a suitable high-level language (CRL). The suite's component tools include a compiler, a transformer-optimizer, an allocator-migrator, a schedulability analyzer, a debugger-monitor, a kernel, and a (simulated) network manager. The overall engineering approach supported by the suite is to provide as simple and natural an integrated development paradigm as possible. The suite tools address complexity due to distribution, scheduling, allocation and other sources in an integrated manner (largely) transparent to the developer. To reflect the needs of propagation of functional and nonfunctional requirements throughout the development process, a number of robust code transformation and communication mechanisms have been incorporated into the suite. To facilitate practical use of the suite, the developed programs compile-transform to a safe subset of C++ with appropriate libraries and runtime support. (In this safe subset (C++) the use of pointers is minimized. Aliases are not allowed.

Deterministic worst-case execution for satisfying hard-real-time constraints, and speculative execution with rollback for improving average-case throughput, appear to lie on opposite ends of a spectrum of performance requirements and strategies. Nonetheless, we show that there are situations in which speculative execution can improve the performance of a hard real-time system, either by enhancing average performance while not affecting the worst-case, or by actually decreasing the worst-case execution time. The paper proposes a set of compiler transformation rules to identify opportunities for speculative execution and transform the code. Moreover, we have conducted an extensive experiment using simulation of randomly generated real-time programs to evaluate applicability and profitability of speculative execution. The simulation results indicate that speculative execution improves average execution time and program timeliness. Finally, a prototype implementation is described in which these transformations have been evaluated for realistic applications.

Engineering of complex distributed real-time applications is one of the hardest tasks faced by the software profession today. All aspects of the process, from design to implementation, are made more difficult by the interaction of behavioral and platform constraints. Providing tools for this task is likewise not without major challenges. In this paper, we discuss a tool suite at New Jersey Institute of Technology's Real-Time Computing Lab which supports the development of complex distributed real-time applications in a suitable high-level language (CRL). The suite's component tools include a compiler, a transformer-optimizer, an allocator-migrator, schedulability analyzers, a debugger-monitor, a kernel, and a (simulated) network manager. The overall engineering approach supported by the suite is to provide as simple and natural an integrated development paradigm as possible. The suite tools address complexity due to distribution, scheduling, allocation and other sources in an integrated manner (largely) transparent to the developer. To reflect the needs of propagation of functional and non-functional requirements throughout the development process, a number of robust code transformation and communication mechanisms have been incorporated into the suite. To facilitate practical use of the suite, the developed programs compile-transform to a safe subset of C++ with appropriate libraries and runtime support.

The overhead of the general checkpointing approach is infeasible for distributed real-time systems where timing is critical. We present a new compiler based approach which classifies data, and minimizes the data needed for checkpointing using static data flow analysis and language support. Our approach provides static guarantees of timeliness while checkpointing, and explores timely recovery for real-time systems. We outline our approach and discuss the necessary architecture and language support needed to make this feasible.

Compiler optimization techniques have been applied to facilitate development and performance timing of non-real-time systems. Unfortunately, regular compiler optimization can complicate the analysis and destroy timing properties of real-time systems. This paper discusses the difficulties of performing compiler optimization in distributed real-time systems. An algorithm is presented to apply machine-independent compiler optimization safely to distributed real-time systems. The algorithm uses resources' busy-idle profiles to investigate effects of optimizing one process on other processes. A restricted form of resource contention is assumed to simplify the analysis.

Achieving fault-tolerance using a primary-backup approach involves overhead of recovery such as activating the backup and propagating execution states, which may affect the timeliness properties of real-time systems. We propose a semi-passive architecture for fault-tolerance and show that speculative execution can enhance overall performance and hence shorten the recovery time in the presence of failure. The compiler is used to detect speculative execution, to insert check-points and to construct the updated messages. Simulation results are reported to show the contribution of speculative execution under the proposed architecture.

The new generation of real-time systems are characterized by multiple, conflicting non-functional desiderata on goals. Furthermore, the systems exhibit very large size and complexity—in both application structures and underlying software and hardware platforms. We argue that current high-level real-time languages do not meet the challenge of these complex real-time systems and introduce a new language—CRL—that we claim does. Relevant real-time features of CRL are discussed and a summary is provided vis-à-vis future features that would address non-functional goals other than timeliness. A current implementation status and how CRL fits into a rather ambitious environment for the construction of complex real-time systems (under construction in our Real-Time Computing Lab at NJIT) are briefly presented.

Deterministic worst-case execution to satisfy hard real-time constraints, and speculative execution with rollback to improve average-case throughput, appear to lie on opposite ends of a spectrum of performance requirements and strategies. Nonetheless, we show there are situations in which speculative execution can probably improve the performance of a hard real-time system, either by improving average performance while not affecting the worst case, or by actually decreasing worst-case execution time. We also show how related strategies for partial or total precomputation can lead to improved performance. Finally, we discuss possible compiler transformations to detect chances of profitable speculative execution.>