Wireless sensor networks are pivotal in critical infrastructure monitoring due to their cost-effectiveness and real-time data processing capabilities. The IEEE 802.15.4 standard, especially the low-energy critical infrastructure monitoring (LECIM) portions, plays a crucial role in enhancing monitoring efficiency of critical infrastructure. LECIM introduces priority channel access (PCA) for traffic prioritization, particularly beneficial for critical information transmission. This article investigates the coexistence of carrier sense multiple access with collision avoidance (CSMA/CA)-based nodes transmitting nonpriority data and LECIM ALOHA PCA nodes handling critical information in unslotted IEEE 802.15.4 networks. We propose a novel theoretical framework to evaluate the performance of a heterogeneous LECIM network, where some nodes use the CSMA/CA channel access method while others use LECIM ALOHA PCA. The performance is measured in terms of reliability, delay and power consumption. Validation of the theoretical framework is conducted using a system-level simulator.

Auto-scaling techniques aim to keep the right number of active servers for the current load: if this number is too small we risk service disruption, but if it is too large we waste resources. Despite the interest in the efficient operation of this type of systems, no prior work has addressed auto-scaling techniques for Network Function Virtualization (NFV) with stringent reliability requirements such as those envisioned in 5G (5 or 6 nines). To achieve such levels of reliability, we need to account for both the activation delay until servers become available (i.e., the wake-up or activation time) and the fallible nature of servers (which may fail with some probability). In this article, we build on control theory to design an auto-scaling technique for a server farm for NFV that guarantees certain reliability while minimizing the number of active resources. We show that the use of well-established tools from control theory results in convergence times much shorter than those obtained with state-of-the-art reinforcement learning techniques. This shows that, despite the current trend to apply machine learning to all sorts of networking problems, there may be some cases where other techniques (such as control theory) can be more suitable.

The softwarization of mobile networks enables an efficient use of resources, by dynamically scaling and re-assigning them following variations in demand. Given that the activation of additional servers is not immediate, scaling up resources should anticipate traffic demands to prevent service disruption. At the same time, the activation of more servers than strictly necessary results in a waste of resources, and thus should be avoided. Given the stringent reliability requirements of 5G applications (up to 6 nines) and the fallible nature of servers, finding the right trade-off between efficiency and service disruption is particularly critical. In this paper, we analyze a generic auto-scaling mechanism for communication services, used to de(activate) servers in a cluster, based on occupation thresholds. We model the impact of the activation delay and the finite lifetime of the servers on performance, in terms of power consumption and failure probability. Based on this model, we derive an algorithm to optimally configure the thresholds. Simulation results confirm the accuracy of the model both under synthetic and realistic traffic patterns as well as the effectiveness of the configuration algorithm. We also provide some insights on the best strategy to support an energy-efficient highly-reliable service: deploying a few powerful and reliable machines versus deploying many machines, but less powerful and reliable.

Standard LoRaWANs leverage pure ALOHA at the medium access control layer, which is proved to be a performance bottleneck as the network size scales up. Stimulated by this fact, this paper studies the applicability and the performance of listen before talk (LBT) medium access schemes in the context of LoRaWANs. We consider two different implementations of LBT: physical layer LBT based on energy detection only and MAC layer LBT based on layer 2 frame decoding, and we propose a Markovian framework to evaluate the performance of LoRaWANs under such setting in terms of data extraction rate and average delay experienced by transmitted uplink messages. The proposed framework is also leveraged to assess the performance of “mixed” LoRaWAN scenarios, where some devices access the channel according to the standard-compliant ALOHA protocol, while other devices transmit according to LBT.

This paper presents a novel Access Point (AP) selection strategy based on a potential game played at a centralized controller. The proposed approach relies on Software Defined Networking (SDN), which has long been considered in the literature as a method to control management functionalities for Wi-Fi networks. The use of SDN provides a global view of the network, which guarantees an efficient distribution of Wi-Fi users among the APs. The centralized potential game proposed in this paper is based on the Fittingness Factor (FF) concept, which is a metric reflecting the suitability of the available spectrum resources to the application requirements. This paper describes the development of a new SDN-based framework that implements the potential game-based algorithm relying on the FF for efficient AP selection. The simulation campaign illustrates the important gains, both in terms of data rates assigned to the Wi-Fi users and their satisfaction compared against the AP selection suggested by the IEEE 802.11 standards and another algorithm proposed in the literature.

We set ourselves from the perspective of a LoRaWAN network operator and we introduce a mathematical programming framework to jointly optimize network layout and network configuration at design time. The proposed framework returns the most cost-effective network layout in terms of gateways position, gateways backhaul configuration and LoRaWAN physical parameters configuration under tight constraints of end node coverage, end-to-end message transmission latency and message extraction rate. Numerical results obtained on realistic network instances demonstrate that the proposed approach leads to network configuration with superior performance with respect to coverage-only classical design policies.

In this work we address the analysis of a LoRaWAN network where some devices access the channel according to the standard-compliant ALOHA protocol, while other devices transmit according to a Listen Before Talk paradigm based on the CSMA/CA mechanism. To analyze this scenario, we propose a mathematical model to obtain the Data Extraction Rate both for CSMA/CA and ALOHA devices, as well as the average delay experienced by messages transmitted by CSMA/CA devices. Simulation results show the accuracy of our model, as well as the benefits of introducing CSMA/CA devices into the network, even when not all the devices implement this mechanism and must coexist with ALOHA devices.

We focus on the problem of managing a shared physical wireless sensor network (WSN) where a single network infrastructure provider leases the physical resources of the networks to application providers to run/deploy specific applications/services. In this scenario, we solve jointly the problems of application admission control (AAC), that is, whether to admit the application/service to the physical network, and wireless sensor network slicing (SNS), that is, to allocate the required physical resources to the admitted applications in a transparent and effective way. We propose a mathematical programming framework to model the joint AAC-SNS problem which is then leveraged to design effective solution algorithms. The proposed framework is thoroughly evaluated on realistic WSNs infrastructures.

Sensor network virtualization enables the possibility of sharing common physical resources to multiple stakeholder applications. This paper focuses on addressing the dynamic adaptation of already assigned virtual sensor network resources to respond to time varying application demands. We propose an optimization framework that dynamically allocate applications into sensor nodes while accounting for the characteristics and limitations of the wireless sensor environment. It takes also into account the additional energy consumption related to activating new nodes and/or moving already active applications. Different objective functions related to the available energy in the nodes are analyzed. The proposed framework is evaluated by simulation considering realistic parameters from actual sensor nodes and deployed applications to assess the efficiency of the proposals.

Increasing the density of access points is one of the most effective mechanisms to cope with the growing traffic demand in wireless networks. To prevent energy wastage at low loads, a resource-on-demand (RoD) scheme is required to opportunistically (de)activate access points as network traffic varies. While previous publications have analytically modeled these schemes in the past, they have assumed that resources are immediately available when activated, an assumption that leads to inaccurate results and might result in inappropriate configurations of the RoD scheme. In this paper, we analyze a general RoD scenario with N access points and non-zero start-up times. We first present an exact analytical model that accurately predicts performance but has a high computational complexity, and then derive a simplified analysis that sacrifices some accuracy in exchange for a much lower computational cost. To illustrate the practicality of this model, we present the design of a simple configuration algorithm for RoD. Simulation results confirm the validity of the analyses, and the effectiveness of the configuration algorithm.

The 802.11e standard enables user configuration of several MAC parameters, making WLANs vulnerable to users that selfishly configure these parameters to gain throughput. In this paper, we propose a novel distributed algorithm to thwart such selfish behavior. The key idea of the algorithm is for stations to react, upon detecting a misbehavior, by using a more aggressive configuration that penalizes the misbehaving station. We show that the proposed algorithm guarantees global stability while providing good response times. By conducting an analysis of the effectiveness of the algorithm against selfish behaviors, we also show that a misbehaving station cannot obtain any gain by deviating from the algorithm. Simulation results confirm that the proposed algorithm optimizes throughput performance while discouraging selfish behavior. We also present an experimental prototype of the proposed algorithm demonstrating that it can be implemented on commodity hardware.

We propose an optimization framework to perform resource allocation in virtual sensor networks. Sensor network virtualization is a promising paradigm to improve flexibility of wireless sensor networks which allows to dynamically assign physical resources to multiple stakeholder applications. The proposed optimization framework aims at maximizing the total number of applications which can share a common physical network, while accounting for the distinguishing characteristics and limitations of the wireless sensor environment (limited storage, limited processing power, limited bandwidth, tight energy consumption requirements). The proposed framework is finally applied to realistic network topologies to assess the gain involved in letting multiple applications share a common physical network with respect to one-application, one-Network vertical design approaches.

Tailbiting convolutional codes are used in several new cellular mobile radio systems such as WiMAX or LTE. This encoding method does not reset the encoder memory at the end of each data block, avoiding the overhead of the zero tail and improving the efficiency. Nevertheless, the absence of a known tail increases the complexity of the decoding process. Since the computational burden of maximum likelihood (ML) decoding is very high, several suboptimal algorithms have been proposed attaining good performance in terms of computational load and error correction capabilities. In this work we propose a decoding algorithm of low complexity which achieves error rates close to those of ML decoding. The proposed algorithm is based on the Circular Viterbi algorithm (CVA) and performs several iterations of the Viterbi algorithm until a tailbiting pattern is found within the path with the highest metric of the extended trellis. Simulations results obtained in an OFDM system under gaussian and wireless channels are near to the performance of the maximum-likelihood decoding and other suboptimal algorithms.

Tailbiting convolutional codes will be used for several applications in new cellular mobile radio systems. This encoding method does not reset the encoder memory at the end of each data block, avoiding the overhead of the zero tail and improving the efficiency. Nevertheless, the absence of a known tail highly increases the complexity of the decoding process. Recently, the use of the A* algorithm has highly decreased the computational complexity of Maximum Likelihood (ML) decoding of tailbiting convolutional codes. The decoding process in this case requires two steps. In the first step, a typical Viterbi decoding is employed to collect information regarding the trellis. The A* algorithm is then applied in the second step, using the information obtained in the first step to calculate the heuristic function. The improvements proposed in this work decrease the computational complexity of the A* algorithm using further information from the first step of the algorithm. This information is used for finding early terminating conditions for the A* algorithm. Simulation results show that the proposed modification decreases the complexity of ML decoding with the A* algorithm in terms of the performed number of operations.

In this work we analyse the performance of a joint coding-precoding scheme for OFDM systems over fading channels. The design of the precoder is based on the information redistribution of the input modulated symbols amongst all the output precoded symbols. The proposed precoder decreases the variance of the instantaneous noise power at the receiver produced by the channel variability. In this sense, the noise at the output of the receiver deprecoding block can be considered as gaussian causing an improvement in the performance of the channel coding scheme employed in the system. The proposed system is evaluated under realistic radio channels which present multipath frequency-selective fadings and are affected by Doppler spread.