Fully Homomorphic Encryption (FHE) enables computations to be performed directly on encrypted data without requiring decryption, providing strong privacy guarantees. However, FHE remains computationally expensive, and writing efficient FHE programs is a complex, error-prone, and time-consuming task that demands significant cryptographic expertise. Programmers are often unaware of available optimizations, and applying them manually requires substantial effort. In this paper, we present CHEHAB, a compiler that automatically vectorizes scalar code, optimizes it, and generates highly efficient FHE programs. CHEHAB supports both structured and unstructured code and takes as input programs written in a domain-specific language embedded in C++. It relies on a Term Rewriting System (TRS) based on equality saturation to simplify and transform programs. CHEHAB targets two key challenges in FHE compilation: (1) automatic vectorization of scalar code, and (2) reduction of instruction execution latency and ciphertext noise growth. By leveraging equality saturation, CHEHAB explores a large optimization space to reduce instruction count and circuit depth while improving vector utilization. Experimental evaluation on a set of representative kernels shows that CHEHAB outperforms Coyote, a state-of-the-art vectorizing compiler for FHE. On average, CHEHAB generates code that is 7.38× faster at runtime, incurs 2.49× less accumulated noise, and achieves 251× faster compilation time. CHEHAB is released as an open-source compiler to support reproducibility and further research in FHE compilation.

Producing powerful deep learning models generally requires a large amount of data, often sourced from a large community of participants. In pervasive health monitoring, privacy concerns arise from the sensitive nature of the healthrelated data shared with remote clouds for training deep models. In this context, different privacy-preserving training solutions can be found in the literature. This paper targets conventional backpropagation algorithm, and addresses privacy preservation under constrained client-side environments and high-accuracy requirement in the context of pervasive health monitoring. It proposes an enhanced privacy-preserving remote training solution based on homomorphic encryption and reversible obfuscation under a non-colluding two-server architecture. The proposed solution can effectively protect sensitive client information from the serverside, with no leakage from gradients or weights during training, while none of the model parameters are revealed to the client. Moreover, the introduced fully reversible obfuscations do not alter training computations, and maintain low computational and communication overhead.

Efficient task execution is critical in mission-driven Edge-Computing (EC)-based IoT applications such as UAV-assisted weather nowcasting. Although UAV fleets improve the resolution and responsiveness of short-term forecasts, their limited on-board computation and battery life demand a cooperative task offloading strategy. Volunteer vehicular edge networks, leveraging idle onboard units of nearby vehicles, offer scalable, low-cost resources. To incentivize task offloading from UAVs to self-interested vehicles, in this work, we introduce a distributed Dutch auction with second-price payments. The proposed scheme is suited for efficient and time-sensitive allocations. Furthermore, to overcome the challenge of non-trivial task valuation, we embed lightweight Reinforcement Learning (RL) agents that dynamically learn valuation policies. Conducted experiments demonstrate that our approach achieves up to $-93 \%$ in information revelation compared to sealed-bid auctions, up to $-50 \%$ the communication overhead of the best baseline, and linear computation complexity, while preserving high social welfare, which is further improved by the integration of RL.

Radio Frequency Identification (RFID) technology is increasingly utilized to provide an efficient identification and tracking capabilities across a wide range of domains. In the medical field, RFID technology enable patient monitoring and equipment management. However, the constrained resources of RFID tags make them vulnerable to security threats, raising concerns about data integrity, secure access, and patient privacy. This paper proposes a novel group-based RFID authentication protocol that introduces a threshold-based mechanism that enables group authentication even when some tags fail to respond. We conduct both formal and informal security analyses using tamarin prover to evaluate the proposed protocol. In addition, we analyze its performance using a thresholdbased availability model. The proposed protocol achieves improved resilience, privacy, and fault tolerance, making it well suited for sensitive Internet of Things (IoT) environments.

Autonomous vehicles (AVs) require extensive simulation testing to ensure safety in complex real-world scenarios. This study presents a comparative analysis of two distinct simulation platforms: CARLA, a purpose-built AV simulator, and Grand Theft Auto V (GTAV), a commercial game adapted for AV research through DeepGTAV. We evaluate both environments using Desertic-specific driving scenarios including sandstorms, dense traffic, and extreme weather conditions. Our methodology combines quantitative metrics (lane deviation, mean speed, stopping distance) with qualitative assessments through GUI-based visualizations. Results show CARLA excels in controlled, reproducible testing with precise environmental control, achieving consistent performance metrics across scenarios. GTAV demonstrates superior realism in unpredictable human behaviors and dynamic interactions but lacks modularity. This research provides critical insights for developers selecting simulation tools for region-specific AV deployment, particularly in Middle Eastern contexts with unique environmental challenges.

Routing is a fundamental function in communication networks. Many metrics have been considered to prolong the network lifetime such as the distance between nodes, and the number of hops. However, existing approaches often overlook the impact of environmental factors, particularly temperature variations, on wireless communication quality. Temperature fluctuations can degrade signal strength, increase bit error rates, and elevate power consumption, adversely affecting network performance. This issue is especially critical in wireless sensor networks (WSNs), where nodes are already equipped with environmental sensors.In this paper, we propose the Temperature-Aware Shortest Path First (TSPF) Protocol, a novel routing approach that accounts for temperature effects to optimize energy efficiency in wireless networks. Empirical studies have demonstrated that higher temperatures negatively impact wireless transmission, leading to increased energy consumption to maintain connectivity over the same distances as in colder conditions. TSPF mitigates these effects by selecting the coldest shortest path available, thereby reducing transmission energy costs and enhancing network longevity. Simulation results show that TSPF significantly extends network lifetime and improves data transmission reliability by optimizing energy use in temperature-variant environments.

In federated learning (FL), achieving high model accuracy often comes at the cost of increased energy consumption, particularly when incorporating robust privacy-preserving techniques like homomorphic encryption (HE). While HE enhances security by enabling encrypted aggregation of local models—thus mitigating data leakage and inference attacks without requiring a trusted aggregator—it also introduces computational overhead that impacts energy efficiency.This paper presents a novel approach for Adaptive Encryption Federated Learning (AEFL). AEFL strategically tunes encryption complexity to achieve the best trade-off between model accuracy and energy efficiency without compromising security. When additional energy from harvested sources is available, AEFL enhances model accuracy by adjusting encryption parameters to support more intensive computations. During periods of limited energy, the system focuses on conserving resources while maintaining robust encryption to ensure data security. This adaptive approach maximizes energy efficiency without lowering the security threshold, allowing for improved model performance when energy conditions permit.Experimental results show that AEFL improves energy efficiency by over 35% and increases the pool of eligible clients for FL by approximately 18%, all while maintaining a security level above 128 bits. Importantly, these gains are achieved with minimal impact on model accuracy, demonstrating that significant energy savings are possible in FL environments without sacrificing data security or model performance.

Homomorphic encryption (HE) enables secure computation on encrypted data, making privacy-preserving machine learning (ML) viable for sensitive applications. Non-linear activation functions (AFs) such as ReLU, Sigmoid, and Tanh remain costly, as traditional polynomial approximations incur deep circuits and long runtimes. We propose a GPU-accelerated method that approximates these activations using fast piecewise-linear functions, formulated as a sign-decision problem to simplify computation and reduce circuit complexity. Compared to existing methods, our technique achieves a lower mean squared error (MSE) to the true activation functions, reducing MSE by approximately $55 \%$ with an error below $10^{-2}$. Experiments demonstrate speedups of up to $128 \times$ on MNIST and $840 \times$ on CIFAR-10, with accuracy comparable to state-of-the-art methods. Across various neural architectures and datasets, our pipeline significantly reduces homomorphic evaluation time and circuit depth compared to polynomial approximations of similar accuracy.

Automatic code optimization enables developers to write high-level code relying on compilers to optimize it and generate efficient code for target hardware. State-of-the-art methods for automatic code optimization leverage deep learning to build cost models that predict the impact of code optimizations on execution time. However, these models are typically limited in terms of the size and complexity of the programs they support. This research presents a novel approach to developing deep learning-based cost models that address these limitations. Our approach introduces a new program representation that efficiently represents programs with complex structures and large sizes such as varying loop depths, buffer numbers, and dimensions. Furthermore, we propose a novel deep learning architecture, that can handle this dynamic program representation. This allows the model to work on larger and more complex programs than those it was trained on. We implemented this model in Tiramisu, a state-of-the-art compiler. Our evaluation shows that our proposed model can generalize to programs larger than those seen during training, while the original Tiramisu cost model cannot. We also show that such generality does not lead to a significant increase in our proposed model’s Mean Absolute Percentage Error or a decrease in the quality of code optimizations found when the model is used for automatic code optimization. In contrast, our proposed model on average achieves a 41.89% improvement in speed compared to the original cost model when both models are trained on the same dataset, showing better generalization over unseen programs. This is a significant advantage over previous approaches, which typically do not support program sizes beyond those seen during the training.

We propose a novel blockchain-based collaborative clustering framework, designed to enhance the efficiency and reliability of data clustering by decentralizing the clustering process across multiple nodes within the blockchain network. The system incorporates a consensus mechanism called Proof of Clustering to achieve the accuracy and consistency of clustering. This results thereby in ensuring confidence and reliability of the final clusters and prevents any breaches or corruption. To improve the performance of clustering process in the decentralized blockchain environment, data parallelism is employed. Through extensive experimentation and performance evaluation, we demonstrate the effectiveness and efficiency of proof of clustering consensus in various scenarios. Comparative studies with traditional centralized and distributed clustering methods showcase the superiority of our proposed solution by in terms of computational speed and resource utilization. PoC improves the Silhouette Score by 5.05% and reduces the Davies-Bouldin Index by 22.93%, indicating better cluster cohesion and separation in the PoC framework.

Massive machine-type communications (mMTCs) network slices in 5G aim to connect a massive number of MTC devices, opening the door for a widened attack surface. Network slices are well isolated, resulting in a low impact on other running slices when attackers control IoT devices belonging to an mMTC network slice (i.e., in-slice attack). However, the impact of the in-slice attacks on the shared infrastructure components with other slices, such as the 5G core network (CN), can be harmful, considering the massive number that can be part of mMTC slice. In this article, we propose a zero-touch security management solution that uses machine learning (ML) to detect and mitigate in-slice attacks on 5G CN components, focusing on Distributed Denial-of-Service (DDoS) attacks. To this aim, we propose: 1) a novel closed-control loop that assists the 5G CN in detecting and mitigating attacks; 2) an ML algorithm that predicts the upper bound of expected MTC devices Attach Requests during a time interval (or an event); 3) a detection algorithm that analyzes an event and uses the ML output to compute a probability that a specific device has participated to an attack; 4) a mitigation algorithm that disconnects and blocks MTC devices suspected to be part of an attack; and (5) a proof-of-concept implementation on top of a 5G facility.

Homomorphic Encryption is one of the most promising techniques to deal with privacy concerns, which is raised by remote deep learning paradigm, and maintain high classification accuracy. However, homomorphic encryption-based solutions are characterized by high overhead in terms of both computation and communication, which limits their adoption in pervasive health monitoring applications with constrained client-side devices. In this paper, we propose PReDIHERO, an improved privacy-preserving solution for remote deep learning inferences based on homomorphic encryption. The proposed solution applies a reversible obfuscation technique that successfully protects sensitive information, and enhances the client-side overhead compared to the conventional homomorphic encryption approach. The solution tackles three main heavyweight client-side tasks, namely, encryption and transmission of private data, refreshing encrypted data, and outsourcing computation of activation functions. The efficiency of the client-side is evaluated on a healthcare dataset and compared to a conventional homomorphic encryption approach. The evaluation results show that PReDIHERO requires increasingly less time and storage in comparison to conventional solutions when inferences are requested. At two hundreds inferences, the improvement ratio could reach more than 30 times in terms of computation overhead, and more than 8 times in terms of communication overhead. The same behavior is observed in sequential data and batch inferences, as we record an improvement ratio of more than 100 times in terms of computation overhead, and more than 20 times in terms of communication overhead.

Unmanned Aerial Vehicle (UAV) applications and services have gained a huge deployment and adoption in different fields, such as the military domain (Defense or reconnaissance) and the civilian domain (Healthcare, surveillance, and transport). UAV operations are generally critical and require, during operations, a control link with the drones, which should be reliable with very low latency. To ensure low-latency, 5G architecture intends to deploy Mobile Edge Computing (MEC) servers, which provide cloud computing capabilities close to the end-users. Consequently, it is envisioned that the AutoPilot application will be deployed at the MEC in order to ensure a low latency connection to the drones. However, the high mobility of drones makes the migration of the AutoPilot applications among MEC servers unavoidable; in order to maintain a low latency connection with the flying drones. This may lead to frequent downtime of the service, which may impact the AutoPilot performances, and hence service migrations should be limited as much as possible. Accordingly, this paper aims to reduce the number of service migrations of drones by introducing novel algorithms that act at the mission planning phase, where the path of the drones is defined.

The Internet of Things (IoT) is an emerging technology that aims to extend connectivity to all everyday devices. One of the main challenges that are slowing down its development is how to secure the Device-to-Device communication. Among all the security issues, the Key Management (KM) is one of the most challenging. The difficulty lies in the fact that most of the IoT devices suffer from a lack of resources. Although different protocols were proposed, most of them do not consider the dynamic nature of the IoT. Other solutions rely on a centralized entity to distribute the new keys upon a change in the network. However, this entity becomes a single point of failure and the main target of attacks. We propose a novel blockchain-based decentralized KM protocol. In addition to being resilient, scalable and dynamic, our solution uses the blockchain technology to securely distribute the KM on several entities.

Named Data Networking (NDN) represents a new communication paradigm, which shifts the Internet towards name-based routing. NDN relies on caching functionalities and local data storage, such as a content request could be satisfied by any node holding a copy of the content in its storage. Network coding, when combined with NDN, allows a data transfer session to use multiple sources for the content seamlessly to improve content delivery efficiency. In this paper, we address the data pollution issue which is inherent to network coding and we propose a single-path authenticated network coding mechanism for Named Data Networking, where a single route between the source to the destination is established. Packets are encoded, cached and signed among this path. First, we formulate our single-path-based optimal coding and homomorphic signature scheme as a mixed integer program (MIP) problem. We consider an optimistic model in which we focus mainly on maximizing defence level of the network. Furthermore, we show how to leverage Software Defined Networking to provide seamless implementation. Finally, we evaluate the efficiency of the proposed coding mechanism, which achieves better performance than conventional NDN with random coding especially in terms of transmission cost, processing overhead and security.

The Internet of Things (IoT) is a network made up of a large number of devices which are able to automatically communicate in a Peer-to-Peer manner. The aim is to provide various services for the benefit of society. One of the main challenges facing the IoT is how to secure this Device-to-Device communication. Among all the security issues, the Key Management is one of the most difficult. This is mainly due to the fact that most of these devices have limited resources in terms of storage, calculation, communication and energy. Although different approaches have been proposed to deal with this problem, each of them presents its own limitations and weaknesses. In this paper, we propose a novel Key Management protocol for Device-to-Device communication in the Internet of Things. Compared to the existing Peer-to- Peer schemes, our solution provides the best compromise between the IoT requirements: resilience, connectivity, efficiency, scalability and flexibility. To achieve this balance, the network members are uniformly distributed into logical sets. A device shares then a distinct pairwise key with each member of its set and a unique pairwise set key with the members of each of the other sets. We then prove that our solution is resilient as the capture of a member compromises a negligible part of a large network. Moreover, we show that our scheme has a good network connectivity. It is then efficient as it does not require additional calculation or communication costs on the network members. We also demonstrate that our protocol is scalable as storage cost on the network members does not significantly increase when the network gets larger. We finally show that our solution is flexible.

In this paper, we focus on versatile and scalable key management for Advanced Metering Infrastructure (AMI) in Smart Grid (SG). We show that a recently proposed key graph based scheme for AMI systems (VerSAMI) suffers from efficiency flaws in its broadcast key management protocol. Then, we propose a new key management scheme (iVerSAMI) by modifying VerSAMI's key graph structure and proposing a new broadcast key update process. We analyze security and performance of the proposed broadcast key management in details to show that iVerSAMI is secure and efficient in terms of storage and communication overheads.

The Internet of Things (IoT) is a network made up of a large number of devices which are able to automatically communicate to computer systems, people and each other providing various services for the benefit of society. These devices have the particularity of being heterogeneous and so have different capabilities in terms of storage, computing, communication and energy. One of the main challenges facing the IoT is how to secure communication between these heterogeneous devices. Among all the issues, the Group Key Management is one of the most difficult. Although different approaches have been proposed to solve it, very few of them consider the heterogeneous nature of the IoT. We propose then a highly scalable Multi-Group Key Management protocol for IoT that ensures the forward and backward secrecy, efficiently recovers from collusion attacks, guarantees the secure coexistence of several services in a single network and balances the loads between its heterogeneous devices according to their capabilities. The evaluation of our solution shows that it is efficient for large-scale heterogeneous networks even if they contain highly resource-constrained devices.

Advanced Metering Infrastructure (AMI) has been regarded as a foundational part of the Smart Grid (SG). Consequently, AMI security is of critical importance. In this paper, we describe and investigate the current proposed authentication schemes and techniques for AMI. We discuss the challenges and desired objectives of authentication. We also provide a review of the recent proposed schemes for AMI along with their advantages and drawbacks towards meeting the discussed challenges and objectives. Based on the current survey, we identify open issues and suggest possible future research directions.

Internet of Things (IoT), coming with billions of connected devices, could potentially transform our daily life but could also create a serious security headache. It brings greater complications in securely accessing these devices with privacy protection guaranteed, and several research issues need to be investigated in detail, e.g., access control, traceability, anonymity, authentication, security bootstrap, etc. Most of the traditional security protection mechanisms are centralized, which make them difficult to scale up to meet the security demands of the IoT.

Internet of Things applications using sensors and actuators raise new privacy related threats, such as drivers and vehicles tracking and profiling. These threats can be addressed by developing adaptive and context-aware privacy protection solutions to face the environmental constraints (memory, energy, communication channel, and so on), which cause a number of limitations for applying cryptographic schemes. This paper proposes a privacy preserving solution in ITS context relying on a game theory model between two actors (data holder and data requester) using an incentive motivation against a privacy concession or leading an active attack. We describe the game elements (actors, roles, states, strategies, and transitions) and find an equilibrium point reaching a compromise between privacy concessions and incentive motivation. Finally, we present numerical results to analyze and evaluate the theoretical formulation of the proposed game theory-based model.

Named Data Networking (or NDN) represents a potential new approach to the current host based Internet architecture which prioritize content over the communication between end nodes. NDN relies on caching functionalities and local data storage, such as a content request could be satisfied by any node holding a copy of the content in its storage. Due to the fact that users in the same network domain can share their cached content with each other and in order to reduce the transmission cost for obtaining the desired content, a cooperative network coding mechanism is proposed in this paper. We first formulate our optimal coding and homomorphic signature scheme as a MIP problem and we show how to leverage Software Defined Networking to provide seamless implementation of the proposed solution. Evaluation results demonstrate the efficiency of the proposed coding scheme which achieves better performance than conventional NDN with random coding especially in terms of transmission cost and security.

Nowadays, multimedia is omnipresent in the Internet and generates the major total traffic in fixed and mobile networks. While video streaming services become more crucial for mobile users, their traffic may often exceed the bandwidth capacity of cellular networks. Content Centric Networking (CCN) can be an attractive solution which adapts the network architecture to the current network usage pattern. In this paper, we propose a CCN peer-to-peer video-on-demand streaming protocol based on scalable video coding. We implement a collaborative strategy which improves the video segments availability in the network and reduces latency relying on CCN functionalities such as caching and routing by name. We also propose a control strategy allowing to scale to highly dynamic networks. Through the tests carried out to evaluate the performance of our solution, we show its effectiveness. Indeed, it reduces significantly the initial playback delay and enhances the streaming quality compared to a traditional service with no collaboration policy.

The proliferation of social networks allowed creating a big quantity of data about users and their relationships. Such data contains much private information. Therefore, anonymization is required before publishing the data for data mining purposes (scientific research, marketing, decision support etc).Most of anonymization works focus on the privacy preserving techniques that allow publishing one instance of the social network without revealing sensitive information. However to analyze the evolution of the social network sequential releases are needed. In this paper we study the problem of privacy in sequential releases of social networks which are represented as labeled bipartite graphs to model the affiliation relationship between users and interests. We propose a solution that allows publishing sequential releases of the same social network while preserving the privacy of data. We consider a set of complex queries to study the utility given by our solution. The experiments demonstrate that the utility of data is preserved as the queries can be answered with reasonable accuracy over the anonymized data.

Radio Frequency Identification (RFID) is a technology for automatic object identification that has been implemented in several real-life applications. In this work, we expand a novel relevant application of RFID tags for grocery stores, which aims to check the adequacy of food items with respect to the shoppers' personal preferences. Unlike similar works, we focus on shoppers' privacy and running time efficiency. For this aim, we propose a novel private set intersection (PSI) protocol to be used in matching the shoppers' personal preferences with the set of each item's adequate profiles that are held by the back-end server of the store. We provide a standard security proof against curious stores and malicious customers. For efficiency concern, we build our protocol without cryptographic operations, and we achieve a linear asymptotic complexity of O (v + c) for communications and store-side computations, where v and c are the numbers of profiles in the store's back-end server and the shopper's list of preferences respectively. Moreover, experimental results and comparisons with state-of-the art solutions reveal the scalability of our novel PSI protocol for big market stores.

Fog computing is a promising technology that ensures sharing resources and services in the neighborhood of a network while enhancing their secrecy and availability. Indeed, sharing through the cloud raises fears when it comes to share sensitive and private data. Many studies show that users, enterprises and stakeholders are more keen to share and collaborate if that sensitive data were managed locally at the edge of the network. Therefore, Fog computing comes to alleviate those fears and allow users to manage sensitive services at the edge of the network. However, since the fog is an extension of the cloud to the edge of the networks, it brings new challenges with respect to implementing security policies relating to shared data and services among multiple consumers, especially when those consumers are dynamic. In this paper, we propose a group key management scheme that allows to manage elastic security domains where dynamic consumers share sensitive services. The elasticity of the scheme supports dynamic adaptation of security domains' size through activating or deactivating proxy services at the fog gateways in order to minimize group key management overheads (1-affects-n and encryption key translation).

Introduction to a special issue of the journal Security and Communication Networks covering security and privacy in emerging wireless networks.

KP-ABE mechanism emerges as one of the most suitable security scheme for asymmetric encryption. It has been widely used to implement access control solutions. However, due to its expensive overhead, it is difficult to consider this cryptographic scheme in resource-limited networks, such as the IoT. As the cloud has become a key infrastructural support for IoT applications, it is interesting to exploit cloud resources to perform heavy operations. In this paper, a collaborative variant of KP-ABE named C-KP-ABE for cloud-based IoT applications is proposed. Our proposal is based on the use of computing power and storage capacities of cloud servers and trusted assistant nodes to run heavy operations. A performance analysis is conducted to show the effectiveness of the proposed solution.

Attribute Based Encryption (ABE) scheme has been proposed to implement cryptographic fine grained access control to shared information. It allows to share information of type one-to-many users, without considering the number of users and their identities. However, original ABE systems suffer from the non-efficiency of their attribute revocation mechanisms. Based on Ciphertext-Policy ABE (CP-ABE) scheme, we propose an efficient proxy-based immediate private key update which does require neither re-encrypting ciphertexts, nor affect other users' secret keys. The semi-trusted proxy assists nodes during the decryption process without having ability to decrypt users' data. Finally, we analyze the security of our scheme and demonstrate that the proposed solution outperforms existing ones in terms of generated overheard.

The Smart Grid (SG) is widely considered to be the informationization of the power grid. Advanced Metering Infrastructure (AMI) has been regarded as a key component of the SG. The critical role of AMI in the SG has made this system a privileged target of cyber attacks. Consequently, AMI security is of very high importance for the security of the SG. For this reason, Key Management has been identified as one of the most challenging topics in AMI development because of the great scale of SG and dynamism of connected clients with respect to tariff programs. This paper proposes a new efficient Multi-group Key management for AMI (MK-AMI) to secure data communications in the smart grid. It is a novel key management scheme that can support unicast, multicast and broadcast communications. An analysis of security and performance, and a comparison of our scheme with recently proposed schemes illustrate that MK-AMI achieves efficient key management and induces low storage and communications overheads compared to existing solutions.

The Internet of Things (IoT) is a rapidly growing technology in recent years. It represents an extension of the Internet into the physical world embracing everyday objects. In consequence, users' privacy and security are becoming a great challenge. To cope with this issue, sophisticated approaches are needed to guarantee these services and hence ensure a large-scale adoption of IoT. In our work we present an approach to model and describe the architecture of internet of things, in which user privacy and security are ensured while taking into consideration the dynamic context in which evolve users, their experience and preferences with respect to security and privacy.

The quality of radio communication links decreases with high temperatures. In this paper, we investigate the effect of temperature on percolation-based connectivity in large scale wireless sensor networks and show that more energy can be saved by allowing some nodes to go to deep sleep mode when temperature decreases and links improve. We determine a closed-form formula for a threshold network density equation in function of temperature τ. Beyond λ*(τ), the network percolates and guarantees connectivity. Based on this result, we propose a simple yet efficient Temperature-Aware MAC plugin (TA-MAC) that enables the underlying MAC protocol to dynamically adapt the network effective density to allow further energy savings while maintaining network connectivity. TA-MAC can be potentially used with any wireless MAC protocol. We carried out simulations and demonstrated that BMAC and SCP-MAC augmented with TA-MAC plugin allow a significant energy efficiency improvement.

Temperature variations have a significant effect on low power wireless sensor networks as wireless communication links drastically deteriorate when temperature increases. A reliable deployment should take temperature into account to avoid network connectivity problems resulting from poor wireless links when temperature increases. A good deployment needs also to adapt its operation and save resources when temperature decreases and wireless links improve. Taking into account the probabilistic nature of the wireless communication channel, we develop a mathematical model that provides the most energy efficient deployment in function of temperature without compromising the correct operation of the network by preserving both connectivity and coverage. We use our model to design three temperature-aware algorithms that seek to save energy (i) by putting some nodes in hibernate mode as in the Stop-Operate (SO) algorithm, or (ii) by using transmission power control as in Power-Control (PC), or (iii) by doing both techniques as in Stop-Operate Power-Control (SOPC). All proposed algorithms are fully distributed and solely rely on temperature readings without any information exchange between neighbors, which makes them low overhead and robust. Our results identify the optimal operation of each algorithm and show that a significant amount of energy can be saved by taking temperature into account.

In this paper we want to explore a new approach for security mechanisms design and deployment in the context of Internet of Things (IoT). We claim that the usual approach to security issues, typical of more classical systems and networks, does not grab all the aspects related to this new paradigm of communication, sharing and actuation. In fact, the IoT paradigm involves new features, mechanisms and dangers that cannot be completely taken into consideration through the classical formulation of security problems. The IoT calls for a new paradigm of security, which will have to consider the security problem from a holistic perspective including the new actors and their interactions. In this paper, we propose a systemic approach to security in IoT and explore the role of each actor and its interactions with the other main actors of the proposed scheme.

We consider the problem of failure detection in networks with energy and communication constraints. Most of current implementations of unreliable failure detectors (FD) use mechanisms to notify process failures in fully connected networks with reliable communication links. This assumption is not applicable to lossy networks. Furthermore, such implementations do not consider resource limitations in terms of energy, storage and bandwidth. This paper presents a new failure detection model for Low-power Lossy Networks (LLN). Our approach defines an adaptive timer-based paradigm. Besides, we introduce two techniques based on the proposed general model. We evaluate all contributions using implementation on Omnet++/Mixim framework. Simulation results demonstrate that our FD enhances detection performances for LLN compared to traditional FD.

Recent advancements in wireless charging technology offer promising alternative to address the challenging problem of energy consumption in low-power networks, as WSN for example. Based on these breakthroughs, existing solutions have investigated wireless charging strategies of low-power networks through the use of mobile chargers, where a charger has to come at the nodes' vicinity to recharge their battery. However, none of these works have considered the multihop energy transmission, whose feasibility have been demonstrated recently. In such a system, a node can transmit energy wirelessly to its neighbors. In this paper, we propose an optimization model to determine the minimum number of chargers needed to recharge the elements of a network in a multihop scenario, taking into account the energy demand of the nodes, the energy loss that occurs during a transfer and the capacity of the chargers. To the best of our knowledge, the work presented in this paper is the first that addresses the optimization of multihop wireless energy transfer in low-power networks.

Given the sensitivity of the potential WSN applications and because of resource limitations, key management emerges as a challenging issue for WSNs. One of the main concerns when designing a key management scheme is the network scalability. Indeed, the protocol should support a large number of nodes to enable a large scale deployment of the network. In this paper, we propose a new scalable key management scheme for WSNs which provides a good secure connectivity coverage. For this purpose, we make use of the unital design theory. We show that the basic mapping from unitals to key pre-distribution allows us to achieve high network scalability. Nonetheless, this naive mapping does not guarantee a high key sharing probability. Therefore, we propose an enhanced unital-based key pre-distribution scheme providing high network scalability and good key sharing probability approximately lower bounded by 1-e-1≈ 0.632. We conduct approximate analysis and simulations and compare our solution to those of existing methods for different criteria such as storage overhead, network scalability, network connectivity, average secure path length and network resiliency. Our results show that the proposed approach enhances the network scalability while providing high secure connectivity coverage and overall improved performance. Moreover, for an equal network size, our solution reduces significantly the storage overhead compared to those of existing solutions.

Given the sensitivity of the potential WSN applications, security emerges as a challenging issue in these networks. Because of the resource limitations, symmetric key establishment is one favorite paradigm for securing WSN. One of the main concerns when designing a key management scheme for WSN is the network scalability. Indeed, the protocol should support a large number of nodes to enable a large scale deployment of the network. In this paper, we propose a new highly scalable key establishment scheme for WSN. For that purpose, we make use, for the first time, of the unital design theory. We show that the basic mapping from unitals to pairwise key establishment allows to achieve an extremely high network scalability while degrading the key sharing probability. Then, we propose a new unital-based key pre-distribution approach which provides high network scalability and good key sharing probability. We conduct analytical analysis to compare our solutions to existing ones, the obtained results show that our approach enhances the network scalability while providing good overall performances. Also, we show that our solutions reduce significantly the storage overhead at equal network size compared to existing solutions.

There has been a host of research works on wireless sensor networks for medical applications. However, the major shortcoming of these efforts is a lack of consideration of data management. Indeed, the huge amount of high sensitive data generated and collected by medical sensor networks introduces several challenges that existing architectures cannot solve. These challenges include scalability, availability and security. In this paper, we propose an innovative architecture for collecting and accessing large amount of data generated by medical sensor networks. Our architecture resolves all the aforementioned challenges and makes easy information sharing between healthcare professionals. Furthermore, we propose an effective and flexible security mechanism that guarantees confidentiality, integrity as well as fine grained access control to outsourced medical data. This mechanism combines several cryptographic schemes to achieve high flexibility and performance.

Tree topologies are widely used in WSN in order to route convergecast traffic to the sink. We consider in this paper the Shortest Path routing Tree (SPT) problem in WSN under different metrics; we show that the basic SPT based strategies are unsuitable for the many-to-one WSN when considering some metrics to compute link costs. Indeed, existing SPT approaches aim to construct a tree rooted at the sink such that the cost of the path from any node to the sink is minimal, while the cost of a given path is computed as summation of the costs of links that compose this path. However, in many-to-one WSN, links which are close to the sink are more critical than other links when using some metrics. We propose in this paper a new weighted path cost function, and we show that our cost function is more suitable for WSN. Based on this cost function, we propose a simple and efficient weighted shortest path tree construction which does not introduce new overheads. We consider, then, the particular case of energy-aware routing in WSN when we apply our new solution in order to construct more suitable energy-aware SPT. We conduct extensive simulations which show that our approach allows to enhance the network lifetime up to 17% compared to the basic one.

Wireless sensor networks for rehabilitation is becoming a topic of great interest in medical applications generating a large amount of work in biomedical and communication research communities. In this line, several solutions have been proposed to provide unobtrusive, flexible and low cost systems for the supervision of rehabilitation. These solutions have focused on sensor/hardware design, platform/architecture design and signal processing algorithms. However, a little attention has been paid to network communications where there exist challenging problems specific to rehabilitation applications. In this paper, we present the design and the implementation of a new light-weight and easy to use wireless sensor network for high-fidelity rehabilitation supervision. Namely, we propose a fault-tolerant, energy-efficient communication protocol that meets the clinical requirements of rehabilitation supervision in terms of data quality and data rate. Also, we outline the implementation of this protocol on the top of the IEEE 802.15.4 standard and evaluate its performance through intensive real-world experiments and simulations.

Wireless sensor networks are becoming a topic of great interest in medical applications generating a large amount of work in medical and computer research communities. In this line, several solutions have been proposed to provide unobtrusive, flexible and low cost systems for patient supervision. However, existing solutions can not be used in rehabilitation supervision because of the specific characteristics of this application. Indeed, new major challenges arise from the fact that each sensor node needs to continuously stream large volumes of data at a high rate to enable doctors to extract clinically relevant information. In addition, the placement of several adjacent sensor nodes on the body may cause serious interferences problems. In this work, we describe and demonstrate our wireless sensor network prototype for high-fidelity rehabilitation supervision. Our demonstration allows attendees to experience the potential of wireless sensor networks for enabling flexible and low cost rehabilitation monitoring systems. In order to alleviate transmission problems, we develop a novel communication protocol that meets the clinical requirements of rehabilitation supervision in terms of data quality and data rate.

In this paper, we propose a fault tolerant protocol based on Directed Diffusion. This latter has the advantage to provide a strong tolerance against node failures thanks to its multipath construction, periodic exploration, and positive/negative reinforcement techniques. Our solution FaT2D (Fault Tolerant Directed Diffusion) defines a new technique which implements a fast failure detection with a prompt path recovery regarding to nodes crash and topology changes. A simulation based comparison between original Directed Diffusion and FaT2D shows that our protocol reduces data loss rate and decreases mean time recovery delay.

We propose, in this paper, a novel class of probabilistic key pre-distribution schemes highly resilient against node capture. We introduce a new approach to enhance resilience by concealing keys through the use of a simple hash chaining mechanism. We provide analytical analysis which shows that our solution enhances the network resilience against node capture without introducing a new overhead comparatively to similar solutions in the literature.

Lately, Peer-to-Peer (p2p) networks like BitTorrent, Limeware and eMule have gained a large popularity as free of charge services to download any kind of files, mostly multimedia contents. Recent reports indicate that near 60 percent of all the current P2P Internet traffic is due to BitTorrent. The music and movies industry, blame BitTorrent among other systems for their falling sales in the USA and Europe, and hence with the help of ISPs started to find ways to throttle their communication. BitTorrent users always found ways to override these obstacles. This ongoing cat and mouse game between ISPs and BitTorrent client developers is about to enter new level. In this paper, we analyze a proposal of a BitTorrent traffic blocking on the network layer and its effects on the peers download efficiency. Our analyses show that in worst case scenarios, our predicted throttling technique could reduce the number of peers succeeding in downloading the copyright protected content to 49 percent.

Key management is a corner stone service for any security solution for WSNs. Resources limitation of WSNs makes the public key based solutions, which offer more efficient key management services, unsuitable for wireless sensor networks. In this paper, we propose a novel efficient tree-based probabilistic key management scheme which is highly resilient against node capture attacks. Our solution is based on symmetric cryptography with a probabilistic key pre-distribution. We introduce a new approach to enhance resilience by concealing keys through the use of a simple hash function mechanism. We further improve resiliency by using the number of shared keys as criteria to construct the secure tree. We provide analytical analysis and extensive simulations which show that our solution enhances the network resilience against node capture without introducing any overhead comparatively to similar solutions in the literature.

Key management, which is an important building bloc in securing group communications, has received a particular attention in both academic and industry research communities. This is due to the economical relevance of group-based applications. The key management concerns the distribution and updates of the key material each time a member joins or leaves the group. The dynamic aspect of group applications due to free membership joins and leaves in addition to members' mobility makes difficult the design of efficient and scalable key management protocols. In this paper, we propose a new key management protocol to secure group communications where we consider the mobility of nodes in a mobile environment with a null rekeying cost. Protocol simulations show that our protocol achieves better performance in terms of rekeying.

Peer-to-peer (p2p) networking technology has gained popularity as an efficient mechanism for users to obtain free services without the need for centralized servers. Protecting these networks from intruders and attackers is a real challenge. One of the constant threats on P2P networks is the propagation of active worms. In 2007, Worms have caused damages worth the amount of 8,391,800 USD in the United States alone. Nowadays, BitTorrent is becoming more and more popular, mainly due to its fair load distribution mechanism. Unfortunately, BitTorrent is particularly vulnerable to active worms. In this paper, we propose a novel worm detection system in BitTorrent and evaluate it. We show that our solution can detect various worm scans before 1% of the vulnerable hosts are infected in worst case scenarios. Our solution, the BitTorrent worm sensor network, is built over a network of immunized agents, which their main job is to efficiently stop worm spread in BitTorrent.

In wireless sensor networks, reliability represents a design goal of a primary concern. To build a comprehensive reliable system, it is essential to consider node failures and intruder attacks as unavoidable phenomena. In this paper, we present a new intrusion-fault tolerant routing scheme offering a high level of reliability through a secure multi-path communication topology. Unlike existing intrusion-fault tolerant solutions, our protocol is based on a distributed and in-network verification scheme, which does not require any referring to the base station. Furthermore, it employs a new multi-path selection scheme seeking to enhance the tolerance of the network and conserve the energy of sensors. Extensive simulations with Tiny OS showed that our approach improves the overall Mean Time To Failure (MTTF) while conserving the energy resources of sensors.

Ensuring security in embedded systems translates into several design challenges, imposed by the unique features of these systems. These features make the integration of conventional security mechanisms impractical, and require a better understanding of the whole security problem. This paper provides a unified view on security in embedded systems, by introducing first the implied design and architectural challenges. It then surveys and discusses the currently proposed security solutions that address these challenges, drawing from both current practices and emerging research, and identifies some open research problems that represent the most interesting areas of contribution.

Energy is a scarce resource in Wireless Sensor Networks. Some studies show that more than 70% of energy is consumed in data transmission. Since most of the time, the sensed information is redundant due to geographically collocated sensors, most of this energy can be saved through data aggregation. Furthermore, data aggregation improves bandwidth usage. Unfortunately, while aggregation eliminates redundancy, it makes data integrity verification more complicated since the received data is unique. In this paper, we present a new protocol that provides secure aggregation for wireless sensor networks. Our protocol is based on a two hops verification mechanism of data integrity. Our solution is essentially different from existing solutions in that it does not require referring to the base station for verifying and detecting faulty aggregated readings, thus providing a totally distributed scheme to guarantee data integrity. We carried out simulations using TinyOS environment. Simulation results show that the proposed protocol yields significant savings in energy consumption while preserving data integrity.

Security and QoS are two main issues for a successful wide deployment of multicast services. For instance, in a multicast streaming application, a receiver would require a data origin authentication service as well as a quality adaptation technique for the received stream. Signature propagation and layered multicast are efficient solutions satisfying these two requirements. In this paper we investigate the use of signature propagation to ensure data origin authentication service. We, then, propose a set of novel data origin authentication techniques for layered media-streaming video. In addition to data origin authentication, the proposed techniques offer continuous non-repudiation of the origin and data integrity. These techniques take advantage of the preestablished layered structure of the encoded video data to reduce the overhead and improve the overall verification in lossy network environments. We evaluate the performance of the proposed techniques through extensive simulations using NS2 simulator.

Even though hierarchical group communication is a prominent communication model for a variety of applications, featured by hierarchical communication rules, it has not been sufficiently investigated in the security literature. In this paper, we introduce private hierarchical group communication and we determine its specific confidentiality requirements, and then we propose an efficient key management protocol satisfying those requirements. This work is done in the frame of a national french project whose consortium includes the international telecom company EADS, INRIA, CNRS and ENST-Paris. The project is called Safe- Cast and deals with group communication in PMR networks that are used mainly by security corps (police, fire fighters, soldiers, and so forth) in areas where it is difficult to have network infrastructures, such as war battles or following a natural disaster (earthquake, tsunami, tornado, or similar).

Many group-oriented applications require authenticating the source of the received traffic, such as broadcasting stock quotes and video-conferencing and hence source authentication is an important component in the whole multicast security architecture. Multicast source authentication must take into consideration the scalability and the efficiency of the underlying cryptographic schemes and mechanisms, because multicast groups can be very large and the exchanged data is likely to be important in volume (streaming). Besides, multicast source authentication must be robust enough against packet loss because most of multicast multimedia applications do not use reliable packet delivery. We propose a new adaptive and efficient source authentication protocol which tolerates packet loss and guarantees nonrepudiation for multicast flows. We have simulated our protocol using NS-2, and the simulation results show that the protocol has remarkable features and efficiency compared to other recent source authentication protocols.

We propose a multicast source authentication protocol based on a novel layered hash-chaining scheme. We called this protocol: receiver driven layered hash-chaining for multicast source authentication (RLH). This protocol tolerates packet loss and guarantees nonrepudiation of media-streaming origin. RLH allows us to save bandwidth as shown in the simulation results section.

IP multicast is increasingly used as an efficient communication mechanism for group-oriented applications on the Internet. This success urged the development of security mechanisms for that communication model. A common drawback of the proposed solutions is that they do not take into consideration the dynamicity of group members. This leads to inefficient solutions for real multicast sessions. In this paper, we first classify proposed protocols for secure multicast and point out their non-suitability for dynamic groups. We then propose an efficient protocol, called AKMP, which maintains good performance by adapting the key management process to the membership frequency during the multicast session. Simulation results show that our protocol is more efficient than existing protocols.