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.

Current security solutions face significant challenges in dealing with the ever-increasing complexity and sophistication of cyber-attacks. This is particularly true for the solutions that inherently rely on deep packet inspection techniques for malicious traffic detection, as almost all the network traffic is now encrypted, rendering these methods ineffective. Recently, there has been a surge of research focused on adopting behavior-based detection techniques, such as Anomaly-based Network Intrusion Detection Systems, that leverage machine and deep learning approaches for detecting malicious encrypted traffic. In spite of their promising results, these approaches are still far from being widely deployed in real-world scenarios. In this paper, we have devised a fully-supervised auto-encoder architecture with a custom reconstruction loss in order to effectively model both benign and malicious encrypted network traffic, with the aim of identifying malicious instances. The experimental results demonstrate that our proposed approach outperforms state-of-the-art methods on the UNSW-NB15 dataset, while achieving comparable performance on the CSE-CIC-IDS2018 dataset.

We consider IoT networks where nodes are able to move to change the network topology and improve area coverage and network performance. We focus on the problem of global optimization where the nodes make use of the predictability of circumstances that affect network operations, such as the communication and sensing ranges, to anticipate future actions that need to be taken so that the correct operation of the network continues to be guaranteed with a minimum global cost. We provide a Mixed Integer Quadratic Program (MIQP)-based solution that minimizes the overall energy consumed over the entire deployment period while maintaining network connectivity and full area coverage. Results show that significant performance enhancement can be obtained when taking predictability into account compared to the case where nodes make decisions based only on their current observations.

We deal with the threats to user privacy in the context of wireless local networks. We focus on location privacy where an adversary tries to learn a user's past and current locations. The current Wi-Fi standard is vulnerable to location privacy and mobility profiling attacks due to the transmission of personally identifying information such as the MAC address in plain text. We provide a generic mathematical model to quantify and express the privacy and elaborate a decentralised algorithm that allows users to attain their desired levels of privacy while lowering its effect on the QoS perceived by them. We evaluate our proposal with numerical simulation and mobility traces collected from Wi-Fi users in an office environment. We show that higher privacy can be obtained with a variable effect on the throughput available to users.

This paper deals with the medium between two reconfigurable sensor nodes characterized by radio interfaces that support multiple channels for exchanging real-time messages under energy constraints. These constraints are violated if the consumed energy in transmission is higher than the remaining quantity of energy. A reconfiguration, i.e., any addition or removal of tasks in devices and consequently of messages on the medium, can cause the violation of real-time or energy constraints at run time. To achieve a feasible scheduling in time (i.e., message deadlines will be respected) and energy (i.e., there is available energy) on the medium, we propose new dynamic solutions: Balance, Dilute, and a Combination of them to manage any addition or removal of messages. The proposed approach utilizes the energy harvesting techniques and the PowerControl algorithm to reduce the nonharvested consumed energy. The proposed strategies achieve significant improvement over existing methods and provide the highest percentage of adding messages, with a lower average in response time and energy consumption. They reach a percentage of success in adding the highest priority messages while meeting deadlines up to 85%.

In a post-disaster situation, the construction of replacement communication infrastructure is crucial for the success of rescue operations. LTE Device-to-Device Proximity Services and IoT are considered as key enabling technologies for the construction of such replacement networks. Existing techniques rely on smartphones as relay stations to build a replacement broadcast-based network that connects available devices. In many cases, the using of such networks require querying a given type of IoT devices (e.g, surveillance cameras, heart-rate monitors, temperature sensors) depending on network users and service requirements. In such scenarios, incorporating all relays in the broadcast is inefficient and may lead to poor network performance. In this paper, we propose constructing for each service type a sub-network of relay stations that ensure connectivity among IoT devices providing the same service type. The resulting sub-networks ensure an efficient and robust message dissemination, avoiding transmission redundancy, and resulting in higher energy savings as well as high coverage. These properties have been validated by implementing our solution in NS-3 by extending the LTE D2D ProSe module provided by NIST. Obtained results show significant improvements in terms of energy consumption, and packet delivery ratio.

Low power and lossy networks (LLNs) require a routing protocol under real-time and energy constraints, congestion aware and packet priority. Thus, Routing Protocol for Low power and lossy network (RPL) is recommended by Internet Engineering Task force (IETF) for LLN applications. In RPL, nodes select their optimal paths towards their preferred parents after meeting routing metrics that are injected in the objective function (OF). However, RPL did not impose any routing metric and left it open for implementation. In this paper, we propose a new RPL objective function which is based on the quality of service (QoS) and congestion-aware. In the case paths fail, we define new RPL control messages for enriching the network by adding more routing nodes. Extensive simulations show that QCOF achieves significant improvement in comparison with the existing objective functions, and appropriately satisfies real-time applications under QoS and network congestion.

In wireless networks based on the IEEE 802.11 standard, mobile stations communicate with access points through the exchange of packets transmitted over the air. These packets can be captured by any listener in the vicinity which can extract the MAC address of the transmitter and thus be able to record the movements of the transmitter over time even when the system uses encryption as layer 2 headers, particularly the MAC address which uniquely identifies a mobile station is not encrypted. Existing solutions to this privacy breach aim at using pseudonyms to avoid using the same MAC address and also introduce silent periods to make it difficult for an attacker to track users. The introduction of silent periods may have negative effects on some applications particularly for those with constraints on bandwidth and delay such as video streaming. In this paper, we propose an enhanced silent period-based solution that allows to maximize the perceived QoS for a preset privacy. Our solution is elaborated from our mathematical model that allows to: (i) quantify the desired user location privacy and QoS, (ii) compute the silent and active periods that cope with these two objectives, and (iii) offer users the best trade-off between privacy and QoS. We experimented our proposal with a set of numerical simulations. The obtained results demonstrated the efficiency of the proposed solution.

We deal with privacy problems resulting from the possibility of communication-flow identification and tracking in the currently designed IPv6 Low Power Wireless Personal Area Network (6LoWPAN)-based solutions for the Internet of Things (IoT). We propose a solution that makes it difficult for an attacker to detect that a communication is taking place between a source and a destination entities by disguising the main flow identifying fields in 6LoWPAN packet headers. Our solution is based on the One Time Address (OTA) approach proposed for the traditional Internet, with a focus on low complexity, memory footprint, and energy consumption. We evaluated our solution that we called micro OTA (uOTA) by implementing it on the Cooja simulator and compared it with micro Moving Target IPv6 Defense (uMT6D). Results show that our solution improves communication flow privacy in IoT and outperforms uMT6D in energy consumption, network overhead, latency, and privacy preservation.

Predicting future spatial and temporal behavior of mobile users is essential for the development of a wealth of new applications and services. In this paper, we focus on predicting the residence time of a user at their relevant locations. We explore the joint use of location history, arrival time, and the previous residence time to accurately predict the residence time at the current location. We developed a model that integrates all these parameters and uses our modified k-moving-average and k-CDF time-aided algorithms that include the arrival time in the model. We run performance evaluation experiments on a large real mobility trace collected by Dartmouth College and made publicly available through the CRAWDAD project. The dataset we worked on included 545 access points and 6.181 users. Our results show that adding high-granularity temporal information to the mobility model allows to significantly improve the residence time prediction compared to state-of-the-art methods. The prediction accuracy improvement for the dataset we work on has been consistent and of about 20% on the average.

We provide a distributed solution to the scheduling of slots over multiple channels in low power wireless sensor networks organized in data gathering tree which are the topologies that are expected to be deployed in the Internet of Things (IoT). Our solution generates a dynamic and distributed schedule that achieves high throughput, low packet errors and collisions, as well as low energy consumption by avoiding deafness and reducing both overhead and overhearing. In our solution, nodes do not communicate large numbers of control message as local topology construction is only based on local link exchange to construct 2-hop neighborhood. To reach our goal, we chose to make use of Combinatorial Design theory, particularly Block Design. Within Block Design, we particularly focused on Latin Squares/Latin Rectangles (LS/LR), and Balanced Incomplete Block Design (BIBD). We evaluate the performance of our solution by comparing it with DiSCA (Distributed Scheduling for Convergecast in Multichannel WSN) and show that we obtain better results in terms of throughput, packet delivery rate, and energy consumption. Our results are based on our implementation of both protocols on NS-3.26.

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.

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.

We propose a new energy efficient network structure for maintaining synchronization in access methods based on Synchronous Preamble Sampling. Our scheme limits the number of synchronization messages and increases network capacity through the use of multiple non-interfering virtual channels. It consists in constructing independent clusters based on the Weakly Connected Dominating Set (WCDS) so that they can use different virtual channels and only need to maintain internal synchronization, while still offering global connectivity. We define a distributed and self-stabilizing algorithm for constructing and maintaining the clusters. Our simulation results show that the proposed scheme has comparable energy consumption to Scheduled Channel Polling, but results in better network capacity. Moreover, it achieves better energy savings and network capacity than recently proposed Crankshaft access method.

One way of improving performance of wireless mesh networks is to use multiple non-overlapping channels. At the same time, the mesh network must continuously self-adapt to varying radio conditions and topology changes. Thus, we propose a neighbor discovery protocol that fits the requirements of multichannel networks while dealing at the same time with the deafness problem. The proposed scheme also takes advantage of neighbor discovery to continually monitor channel activity. We provide a theoretical analysis of the average discovery time. Our simulations show that the solution integrated within Molecular MAC, a multichannel MAC, results in efficient discovery and channel assignment.

Multichannel solutions are increasingly used to cope with the problem of low capacity in sensor networks resulting from high contention during wake-up periods of nodes. We can consider wake-up schedules as virtual channels, because nodes using different schedules cannot communicate with each other. In this paper, we show that multiple virtual channel solutions come at the cost of an increased energy consumption in broadcasts. We analyze the problem of broadcasting over multiple virtual channels under different classes of MAC methods and propose Clustered Virtual Channels (CVC), a new network structure that limits the number of frames needed for maintaining multiple virtual channels synchronized. Our simulation results show that CVC reduces the cost of maintaining synchronization and increases capacity while making all types of broadcasts possible.

We provide an analytical framework for preamble sampling techniques for MAC protocols in wireless sensor networks, from which we derive closed-form formulas for lifetime and reliability calculations. In addition to take into account transmitter behavior that controls the form and the content of the transmitted preamble, our model also considers receiver behavior that controls the duration of preamble reception in case of successful and failed reception. Along with both transmitter and receiver behavior, our model considers a non-perfect channel and thus takes into account the impacts of transmission errors and retransmissions on lifetime and reliability of preamble sampling protocols. Numerical results show that no protocol is universally optimal; that is, each protocol has its own optimal operation point that depends on the given channel and load conditions.

MAC protocols based on preamble sampling techniques like WiseMAC and BMAC offer significant energy savings for multihop wireless sensor networks. Although preamble sampling based techniques efficiently reduce idle listening in low-rate data networks, they introduce extra overhead due to possible reception of some irrelevant frames. In this paper, we identify the problem of receiving irrelevant frames and propose to replace the continuous preamble by a series of small frames that we call micro-frames. The resulting Micro-Frame Preamble (MFP) scheme makes it possible for a node to switch its radio off to avoid receiving irrelevant frames and improve energy savings. In the paper, we evaluate the theoretical energy savings of MFP compared with traditional preamble sampling techniques. We also discuss some experiences with our implementation of MFP on the Freescale MC13192 SARD.

Previous studies have shown that preamble MAC protocols have higher energy efficiency compared to traditional low power MAC protocols based on common sleep/wakeup schedules. One efficient implementation of preamble MAC protocols is frame preamble MAC in which the MAC preamble is replaced by a series of frames. In this paper, we consider four frame preamble MAC protocols, namely: persistent MFP (micro frame preamble), non persistent MFP, persistent DFP (data frame preamble), and non persistent DFP. We study their energy cost and communication reliability by assuming a simple binary symmetric channel error model

Wireless sensor networks (WSNs) have witnessed a tremendous upsurge in recent years, both in academia and industry; this is mainly attributed to their unprecedented operating conditions and a variety of commercially viable applications. Because of their dependence on scarce battery power, communication protocols need to be energy efficient. However, finding the optimal solution is challenging as it needs to consider the whole communication stack at once. In this paper, we propose an approach that aims at optimizing jointly L2 (link) and L3 (routing) protocols. We design 1-hopMAC, a communication architecture grouping MAC and routing layers which avoids 1-hop neighborhood knowledge. 1-hopMAC can be combined, among others, with a geographic or gradient based routing protocols. We present an analytical study of energy consumption to point out the optimal configuration of 1-hopMAC

We present the use of an appropriate metric-mapping function to turn a pre-existing min-delay routing algorithm into max-min routing. Some candidates for such a mapping function are presented, and their performances are discussed. We then apply this idea to make directed diffusion sensitive to the level of energy remaining in the forwarding nodes. The resulting algorithm, localized max-min remaining energy routing (LMMER), is then implemented in a network simulation, and we show that it significantly prolongs the life of energy-critical nodes.

IVG (inter vehicle geocast) is a GPS-based inter-vehicle communication mechanism used for alarm message dissemination among vehicles in a highway in risk situations. In this paper, we propose an improvement to IVG towards supporting its interoperability in environments where "GPS-U" GPS-unequipped vehicles are present. They are vehicles that are not equipped with GPS devices or they have some problems to obtain their positions, via GPS, due to some obstacles. The proposed solution allows GPS-U vehicle to compute its position with the help of its neighbors that are equipped with GPS devices, called GPS-E neighbors. Analyses show that the optimal performances of IVG can be reached even when the rate of GPS-U vehicle is 40%.