Cooperative, Connected, and Automated Mobility (CCAM) is based on fast, secure, and reliable Vehicle-to-Everything (V2X) communication to enable collective perception and maneuver coordination in autonomous driving. However, high-frequency wireless communication, particularly in the millimeter wave (mmWave) and terahertz (THz) bands, is highly susceptible to environmental obstacles, leading to severe signal attenuation and communication delays. This study investigates the integration of Reconfigurable Intelligent Surfaces (RIS) into vehicular networks to mitigate these challenges and analyzes their impact on Age of Information (AoI) and Peak AoI (PAoI) metrics. The latter metrics are highly relevant to feedback delays introduced in the Cooperative Control Schemes of Autonomous Vehicles, which are well known to compromise their stability. Their characterization is thus crucial for the performance assessment of the automatic controllers with this characterization in the presence of RIS not investigated as of now in the literature. Using the All-or-Nothing Receiver Model (ANRM) and Distance-Dependent Propagation Model (DDPM), we extend AoI formulations by incorporating RIS path loss characteristics revealing that RIS-assisted networks significantly reduce AoI and PAoI values, enabling reliable cooperative control in vehicular systems. We consider a cooperative merging maneuver on an intersection as our test case and demonstrate that the effect of the introduced delays on the cooperative control scheme performance is minimal. Passive RISs are assumed throughout the study, due to their energy efficient operation as compared to active metasurfaces.

Vehicular networks are a crucial component aimed to revolutionize the transportation system through the integration of several services and technologies including autonomous driving, dynamic routing, real-time traffic monitoring, and onboard entertainment systems. These services necessitate robust computational resources, seamlessly fulfilled by mobile edge computing (MEC) trough the roadside units (RSUs). MEC excels in offering low-latency with real-time data access, which is critical for these applications. As we gear up for the advent of 6G networks, which will operate at millimeter-wave and terahertz frequencies, the challenge of signal loss becomes significant. To this end, this paper propose a novel 6G latency aware computational offloading framework that strategically deploys Reconfigurable Intelligent Surfaces (RIS) between autonomous vehicles and RSUs. Our approach leverage cooperative interactions among RSUs, which enhances overall service performance and significantly reduces latency. Both of these factors are crucial for providing efficient MEC environment in vehicular networks. Our proposed method has been implemented and tested where the results shows that our approach achieves 5 to 7 seconds reduction in time delay compared to the state-of-the-art approaches.

Network traffic classification is crucial for optimal network resource management. Several network traffic classification methods have been proposed, e.g., Deep Packet Inspection (DPI), and machine learning-based network traffic classification. Each approach is generally efficient for a certain class of network traffic. However, there is no one-fit-all method, i.e., no method offers the best performance for all types of network traffic. In this paper, we propose a hybrid network traffic classification technique that uses a combination of DPI and machine learning to identify and classify the network traffic into different Quality of Service (QoS) classes. The traffic is first identified through the DPI module, and the unidentified traffic then goes through the machine learning module, offering a classification accuracy of more than 98%. The results are evaluated based on the combination of DPI and different machine learning methods, e.g. supervised and unsupervised learning algorithms.

Quality of service (QoS), ubiquitous connectivity and efficient utilization of spectrum are the main concerns for vehicular users inside public transport (buses, trains etc.) in moving environments. Deployment of spectrum efficient Non-orthogonal multiple access (NOMA) enabled moving small cells can be a promising solution. However, resource allocation and pairing of backhauls for moving small cells is a challenging task due to load of small cells and Doppler effects created by time varying channel. Thus, to incorporate the mobility effect and increasing demand of spectrum, in this work we investigate a hybrid architecture for load based joint backhaul pairing and resource allocation of NOMA enabled moving small cells that maximizes spectral efficiency (SE) during on-peak instants and minimizes system complexity during off-peak instants. Since, the optimization problem formulated is NP-hard, therefore we solve it with the help of a heuristic, storage based moving small cell pairing algorithm (SMSPA). Our performance evaluations show that our proposed scheme, SMSPA, outperforms the benchmark pairing schemes in terms of data rate, bandwidth occupancy, SE during on-peak instants and reduce time complexity during off-peak instants.

The interplay of physical layer enhancement due to Successive Interference Cancellation (SIC) and classic random access protocols used in most multi-access communication channels is the object of this paper. Considering the classic representatives of random access protocols, Slotted ALOHA and Channel Sensing Multiple Access (CSMA), we show that two operation regimes can be identified as a function of the communication link spectral efficiency. In case of high levels of spectral efficiency, multi packet reception as granted by SIC is of limited usefulness. The obtained overall sum-rate performance are dominated by the MAC protocol algorithm. On the contrary, for low spectral efficiency levels, sum-rate performance are essentially dependent on physical layer SIC capability, while the MAC protocol is of marginal. Limitations due to limited transmission power level dynamic range are shown to induce unfairness among nodes, i.e., nodes closer to the sink achieve better sum-rate and success probability performance. However, the unfairness issues fades away when the system is driven to work around the sum-rate peak achieved for low spectral efficiency communications. This is a major finding of this work: apparently, SIC can boost performance while still maintaining a fair sharing of the achieved sum-rate performance among nodes, irrespective of the quality of their respective communication channels.

Metasurfaces constitute a revolutionary technology for the realization of intelligent reflecting surfaces (IRS) which can alleviate the blockage problem in mmWave and Thz communications in the absence of Line of Sight (LOS). In this work, we consider the use of multiple IRSs to provide LOS paths between a sender and a receiver via reflection. Unlike previous work, we use the directivity as a means to incorporate the metasurface reflection behavior in the channel model and parameterize with respect to the design parameters. The design problem considered is the choice of the “best” IRSs for consecutive reflection of the transmitted signal to optimize the communication channel. The problem is formulated as an optimization problem which is challenging to solve due to the dependence of each link cost on the previous link. We consider a relaxation which decouples the link costs, we apply Dijkstra’s algorithm for the solution and we show that the performance degradation as compared to the original problem which is solved using exhaustive search is not significant.

The Internet-of-Things (IoT) is an imminent and corporal technology that enables the connectivity of smart physical devices with virtual objects contriving in distinct platforms with the help of the internet. The IoT is under massive experimentation to operate in a distributed manner, making it favorable to be utilized in the healthcare ecosystem. However, under the IoT healthcare ecosystem (IoT-HS), the nodes of the IoT networks are unveiled to an aberrant level of security threats. Regulating an adequate volume of sensitive and personal data, IoT-HS undergoes various security challenges for which a distributed mechanism to address such concerns plays a vital role. Although Blockchain, having a distributed ledger, is integral to solving security concerns in IoT-HSs, it undergoes major problems, including massive storage and computational requirements. Also, Holochain, which has low computational and memory requirements, lacks authentication distribution availability. Therefore, this paper proposes a hybrid Holochain and Blockchain-based privacy perseverance and security framework for IoT-HSs that combines the benefits Holochain and Blockchain provide, overcoming the computational, memory, and authentication challenges. This framework is more suited for IoT scenarios where resource needs to be optimally utilized. Comprehensive security and performance analysis is conducted to demonstrate the suitability and effectiveness of the proposed hybrid security approach for IoT-HSs in contrast to the Blockchairi-only or Holochairi-only based approaches.

Wireless use cases in the industrial Internet of Things networks often require guaranteed data rates ranging from a few kilobits per second to a few gigabits per second. Supporting such a requirement in a single radio access technique is difficult, especially when bandwidth is limited. Although nonorthogonal multiple access (NOMA) can improve the system capacity by simultaneously serving multiple devices, its performance suffers from strong device interference. In this article, we propose a Q-learning-based algorithm for handling many-to-many matching problems, such as bandwidth partitioning, device assignment to sub-bands, interference-aware access mode selection [orthogonal multiple access or NOMA], and power allocation to each device. The learning technique maximizes system throughput and spectral efficiency (SE) while maintaining quality-of-service (QoS) for a maximum number of devices. The simulation results show that the proposed technique can significantly increase overall system throughput and SE while meeting heterogeneous QoS criteria.

The staggering growth of the number of vehicles worldwide has become a critical challenge resulting in tragic incidents, environment pollution, congestion, etc. Therefore, one of the promising approaches is to design a smart vehicular system as it is beneficial to drive safely. Present vehicular system lacks data reliability, security, and easy deployment. Motivated by these issues, this paper addresses a drone-enabled intelligent vehicular system, which is secure, easy to deploy and reliable in quality. Nevertheless, an increase in the number of operating drones in the communication networks makes them more vulnerable towards the cyber-attacks, which can completely sabotage the communication infrastructure. To tackle these problems, we propose a blockchain-based registration and authentication system for the entities such as drones, smart vehicles (SVs) and roadside units (RSUs). This paper is mainly focused on the blockchain-based secure system design and the optimal placement of drones to improve the spectral efficiency of the overall network. In particular, we investigate the association of RSUs with the drones by considering multiple communication-related factors such as available bandwidth, maximum number of links a drone can support, and backhaul limitations. We show that the proposed model can easily be overlaid on the current vehicular network reaping benefits of secure and reliable communications.

The Intelligent Transportation System (ITS) has become essential for the economical and technological development of a country. The maturity of communication technologies (Vehicle to Infrastructure (V2I) and Vehicle to Vehicle (V2V)) and the amalgamation of smart grids, electric vehicles (EVs) and energy trading resulted in a storm of research opportunities for green ITS. In addition, the combination of vehicular communication technologies and ITS enable efficient selection of EV charging stations (CS) and scheduling EVs charging requirements in real-time. However, the untrusted centralized nature of energy markets and EV charging infrastructures result in several privacy and security threats to EV user's private information. These security and privacy threats include targeted advertisements, privacy leakage, selling data to third party, etc. In this work, we propose BlockEV, a blockchain-based efficient CS selection protocol for EVs to ensure the security and privacy of the EV users, availability of the reserved time slots at CSs, high Quality of Service (QoS) and enhanced EV user comfort. First, a blockchain-based framework is introduced to implement secure charging services and trusted reservation for EVs with the execution of smart contract. Second, we focus on the efficient CS selection and propose a mechanism for EVs to select the CS locally without sharing private information to CS, while fulfilling their service requirements. Evaluations show that the proposed BlockEV is scalable with significantly low blockchain transaction and storage overhead.

In this paper, we present a distributed machine learning based intrusion detection system in Internet of Things (IoT) utilizing Blockchain technology. In particular, spectral partitioning is proposed to divide the IoT network into autonomous systems (AS) enabling traffic monitoring for intrusion detection (ID) to be performed by the selected AS border area nodes in a distributed manner. The ID system is based on machine learning, where a support-vector machine algorithm is trained using prominent IoT data sets and detection of the attackers is provided. Furthermore, the integrity of the attackers' list is offered by utilizing Blockchain technology, which enables a distributed sharing of the attackers' information among the AS border area nodes of the Blockchain network. Simulations are performed to evaluate different aspects of the proposed IoT system and demonstrate the potential of integrating machine learning based ID to a distributed spectral partitioned Blockchain network.

Video share of the Internet traffic is increasing day by day. This includes streaming on the go to/from mobile devices. These trends necessitate dynamic and robust resource allocation at Internet Exchange Point level to provide good quality of services to mobile video users. Any effective solution to this problem requires accurate predictions of the video traffic coming from or delivered to mobile devices. In this paper, we propose a framework to correctly identify and accurately predict the live streaming and video traffic. Deep packet inspection is used to identify the 23 most common live streaming and video traffic protocols. Subsequently Long Short-Term Memory neural network is used to predict the live streaming and video traffic over a prediction horizon of 6 hours with an average accuracy of 97.24% thus outperforming previous frameworks in both the accuracy and the prediction horizon. This technique can be used as a baseline towards a more effective application of traffic engineering techniques.

Unmanned aerial vehicles (UAVs) are expected to be extensively used as an integral part in the future generations of communication networks, to provide ubiquitous connectivity. The mobile nature of UAVs make them a tempting candidate to provide seamless connectivity in environments where the installation of conventional terrestrial base stations (BS) is not feasible. Nonetheless, there are major deployment issues related to optimal placement of UAV-mounted base stations (UAV-BSs) due to limited number of UAV-BSs, limited energy availability and trade-off between coverage area and its altitude. In this paper, we address UAV-BSs placement issues by proposing a novel Machine learning (ML) based intelligent deployment mechanism. More specifically, for intelligent deployment of UAV-BSs based on energy, computational power, nature of available data and criticality of the scenario, we use two different approaches: Support Vector Machine (SVM) and Deep Learning (DL), which is composed of sequential time series learning process. Moreover, to address the security and privacy challenges emanating from the wireless connectivity and untrusted broadcast nature of UAV-BSs, we propose a Blockchain-based novel information-sharing scheme. To evaluate the performance of our combined secure and intelligent proposed approach, we have improved energy consumption by almost twice in contrast with the normal deployment of UAV-BSs.

The 5th generation (5G) of communication networks will facilitate innovative and emerging services and applications having lower latency requirements, increased energy efficiency and reliability. These characteristics of 5G make it capable to act as a potential underlying network for smart city services such as for implementation of demand response in smart grids. More specifically, in terms of demand response, these low latency networks are used for the explicit exchange of messages between utility companies and customers for pricing mechanisms. According to the time to use (ToU) pricing scheme, consumers are offered a specific electricity price for each time interval i.e., off-peak, on-peak and mid-peak blocks. Unlike high energy consumers (HECs), low energy consumers (LECs) are not the reason of high peaks (on-peaks) formation; however, they pay higher rates to the utility during on-peak hours because of one price for all rule. Here, one price for all makes ToU an unjustified pricing scheme. This issue is discussed in this study and a fair pricing scheme is proposed to remove undue financial burden from LECs. The proposed fair pricing scheme (FPS) is based on energy consumption of each category customer. LECs and HECs pay the electricity bill exactly according to their electricity consumption and no one has to bear the financial load of others. Simulation results show that LECs are able to save up to 11.0075% of their total electricity bill. HECs has to pay the penalty of high energy consumption whereas, the utility company is not affected by the implementation of the proposed fair pricing scheme.

With the emergence of IoT era and the increased pressure on networks placement of small cells in cellular architecture plays a significant role in boosting the system throughput while truncating the power and energy consumption of the network. To meet the growing demand of data traffic, wireless content caching can be cost effective solution in terms of backhaul energy and capacity while providing effective solution to meet the user demand and reduce the traffic in small cell network. Energy efficiency is a major concern for sustainable development of wireless networking-based IoT. Factors affecting the energy efficiency (EE) of the network are backhaul capacity, power and energy limitation, at the macro base stations. In this paper, we have investigated a file placement strategy in a cooperate small cell network with an objective of maximizing EE. We propose an energy efficient cooperative caching scheme (EECCS) in which files are placed in cooperative small cell base station (SBS) in such a manner that small cell base station can access files from the caches of other small cell base station to maximize the energy efficiency. Numerical results show that energy efficient cooperative caching scheme provides results that validate and characterize the implementation of the proposed algorithm (EECCA).

Wireless backhaul of outdoor small cells is a cost-effective solution in a dense heterogeneous network as it reduces the need to provide a wired connection for each small cell access point to the core network. On the other hand, non-orthogonal multiple access (NOMA) has emerged as a promising technology to improve the spectral efficiency of a network. This paper investigates the impact of applying NOMA at the backhaul of small cells to enhance the spectral efficiency of the system. However, this requires a careful pairing of desired small cells to increase the system performance of NOMA. In this regard, a joint pairing and resource (bandwidth and power) allocation scheme for the backhaul of small cells is studied based on the load of the small cells. Furthermore, our performance evaluation shows that the proposed scheme outperforms the existing user pairing approaches in terms of achieving high spectral efficiency.

Low power wide area network (LPWAN) technology has been widely adopted in different Internet-of-things (IoT) services. Long range wide area network (LoRaWAN) is an evolution of wireless sensor network (WSN) directed to IoT concept, mostly used in private outdoor applications. The existing LoRaWAN operates following the simple ALOHA standards. Therefore, it suffers from high packet loss and supports a very limited number of nodes. For the application of LoRaWAN in dense networks, an efficient channel access mechanism is required in order to improve the efficiency and robustness. In this paper, we investigate a modified listen-before-talk (LBT) mechanism. Specifically, we propose LoRa-BED, LoRa-BEB and LoRa-BEH, channel access protocols to reduce collisions in high density environment. Our results demonstrate that the proposed protocols significantly improve the channel utilization and efficiency with a slight increase in energy per device while sensing the channel.

Cyber-physical Systems (CPS) comprise of a network of physically distributed embedded sensors and actuators equipped with computational and communication capability. In CPS, Internet of Things (IoT) devices communicate in a trustless environment as the data can be compromised due to the centralized database, limited power and computational constraints. At the same time, reliability and resiliency are key concerns in CPS in the face of unforeseen circumstances, often emanating from disaster based failures. In this paper, critical issues of centralized database security in CPS are addressed via a distributed blockchain based solution. The proposed system encompasses a smart contract based framework in Ethereum blockchain. It further explores the potential of blockchain in securing and offering a distributed network for the CPS in a closed loop manner. To demonstrate the realizability of the proposed framework, a testbed implementation for the proposed idea is provided. A desktop computer, a laptop, a simple temperature sensor and a Light Emitting Diode (LED) are interfaced in a Peer-to-Peer (P2P) network using Ethereum. The speedy transaction of sensor data in the blockchain at various difficulty levels and actuation through smart contracts enhance the usability of the platform for CPS and various IoT applications.

The Tactile Internet is envisioned to be characterized by ultra low latency, ultra reliability with high availability, and security. The next fifth generation is expected to underpin this emerging technology at the wireless edge. The stringent low latency and high reliability requirements of the Tactile Internet render the design more challenging in the presence of energy constraints on Internet of Things device in the underlying device-to-device network. In this paper, to deal with the stringent delay requirements of the tactile communication in the presence of energy constraints on devices and nodes, we pose the combined energy management and rate control problem as a queue control problem, where the objective is to control the energy and data queues to predefined reference levels. The intelligently controlled energy queues make the communication reliable and guarantee that nodes of the network are always alive and have some amount of energy close to predefined reference values for emergency and critical operations. In addition, tight control of the data queue ensures low queuing delays. We have considered both linear and nonlinear model of queues and have designed controllers based on model predictive control and nonlinear control theory. Convergence properties of the controllers are established analytically and the effectiveness of the proposed methods is also demonstrated through simulations.

Smart grid is a next generation electricity network that transmits electricity to end users and enable two way digital communication for providing remote reading and other advanced metering functions. For Smart grid domains to interact with each other, a reliable communication is required. This reliable two-way communication can be achieved with the use of small cells technology (femto, pico, etc), relay, or any of the transmission scheme proposed in 5G systems. With the use of femto cells and relay nodes in the network, the allocation of resources for the purpose of smart grid communication needs to be done efficiently. Power being one of the resource has been studied for its allocation in a femto cell network for Smart grid communication, however, transmission reliability and interference is generally ignored. In this paper, we investigate the problem of power allocation in a Neighborhood Area Network (NAN) with femto cell and relay technology as a communication mechanism. We formulate the problem as a power minimization problem such that the transmission reliability is ensured. Our performance evaluation shows that the average transmit power required for transmission through femto cell is 99% less as compared with cooperative transmission.

LoRaWAN is a Low Power Wide Area Network (LPWAN) protocol designed to allow low power battery operated nodes to communicate with each other. Though LoRaWAN provides end-to-end security, however vulnerabilities exist in the security mechanism of LoRaWAN join procedure. A jammer can be used to launch a denial of service (DOS) attack by permanently disconnecting the LoRa end nodes from the LoRaWAN network. In this paper, we propose a novel LoRaWAN based Intrusion Detection System (LIDS) for jamming attacks. A real experimental testbed is developed and deployed and LIDS is trained on real join request data. We propose two LIDS algorithms based on Kullback Leibler Divergence (KLD) and Hamming distance (HD). The algorithms are extensively tested on realworld dataset. Receiver Operating Characteristic (ROC) based performance evaluations show that KLD and HD can achieve detection rates as high as 98% and 88% respectively with 5% false positive rate.

Due to scarce energy resources and large power consumptions, Wireless Power Transfer (WPT) technology is gaining increasing interest in 5G networks and the Internet of Things (IoT). However, when data communication is conducted concurrently with WPT, large interference signals may be induced which degrade performance significantly. In LoRaWAN networks, a popular IoT technology, the problem is further exacerbated by the potential of the interference signals to jam the join procedure by flattening the Received Signal Strength (RSS), thus compromising system security. In this work, we develop a mathematical framework which allows the characterization of the join procedure and communication in LoRaWAN networks as a result of the interference effects induced by WPT devices. The conducted analysis and the relevant numerical results are used to characterize the interference effects and join procedure failure ratio with respect to the power, distance and number of WPT devices. The flattening effects of RSS are shown via simulations and its implications on the network security are also demonstrated. The presented work aims at providing design guidelines for the system, so that communication performance degradation as well as network security deterioration as a result of the WPT effects is kept within acceptable levels.

MAC layer adaptation is very crucial for supporting dense and diverse data requirements of sensor networks in smart cities, powered by energy harvesting. In this paper, we perform MAC layer optimization for maximizing throughput subject to application-specific needs and energy availability in Solar Energy Harvesting Wireless Sensor Networks (EH-WSNs). In contrast to previous schemes that limit energy consumption based on current availability only, we propose Energy Prediction based Energy Management algorithm (EPEM). This algorithm exploits energy prediction and sets threshold rate of energy consumption to ensure accumulation of sufficient energy for non- energy harvesting period. Our analysis shows that MAC optimization (MO) along with EPEM algorithm not only improves performance by 72% but also avoids energy scarcity during non-energy harvesting period.

Device-to-device (D2D)-enabled Internet-of-things promises higher spectral efficiency and system capacity by sharing the cellular spectrum and offloading the cellular traffic. However, it poses new challenges in terms of resource allocation because of interference from D2D to base station and vice versa in the downlink. Moreover, energy efficient operation of the network becomes a major challenge because of unattended operation of devices. In this study, we investigate resource allocation for the energy-harvesting-aided D2D communication underlaying cellular network. We formulate a joint subcarrier assignment and power allocation problem for multiple cellular and energy harvesting direct D2D links to maximize the overall sum rate subject to quality of service, subcarrier reuse, power, and energy harvesting constraints. The problem is formulated as a mixed integer nonlinear programming and is difficult to be solved in polynomial time. Therefore, a low complexity algorithm named energy harvesting and gain-based resource allocation (EHGRA) is proposed, which determines reuse partners considering interference among D2D and cellular links, and then, allocates power optimally to them such that the sum rate is maximized. Our performance evaluation demonstrates that energy harvesting can boost up the system performance and at the same time can achieve higher sum rate over short frame duration. Comparison shows that EHGRA performs better than the existing algorithms in terms of overall sum rate.

We review harvested energy prediction schemes to be used in wireless sensor networks and explore the relative merits of landmark solutions. We propose enhancements to the well-known Profile-Energy (Pro-Energy) model, the so-called Improved Profile-Energy (IPro-Energy), and compare its performance with Accurate Solar Irradiance Prediction Model (ASIM), Pro-Energy, and Weather Conditioned Moving Average (WCMA). The performance metrics considered are the prediction accuracy and the execution time which measure the implementation complexity. In addition, the effectiveness of the considered models, when integrated in an energy management scheme, is also investigated in terms of the achieved throughput and the energy consumption. Both solar irradiance and wind power datasets are used for the evaluation study. Our results indicate that the proposed IPro-Energy scheme outperforms the other candidate models in terms of the prediction accuracy achieved by up to 78% for short term predictions and 50% for medium term prediction horizons. For long term predictions, its prediction accuracy is comparable to the Pro-Energy model but outperforms the other models by up to 64%. In addition, the IPro scheme is able to achieve the highest throughput when integrated in the developed energy management scheme. Finally, the ASIM scheme reports the smallest implementation complexity.

Unlike traditional cellular networks, the multi-tier architecture of 5G networks supports Device-to-Device (D2D) communication, which allows devices to communicate with each other autonomously. Consequently, D2D-enabled Internet of Things (IoT) promise to provide higher bandwidth through frequency spectrum sharing. However, energy efficient operation of the network becomes a major challenge due to unattended operation of devices. In this study, we propose a solar energy harvesting based model for throughput maximization of an overlay in-band D2D network and consider an energy prediction model for optimal power management. Sum rate maximization problem subject to energy and power constraints is formulated for multiple D2D pairs and maximum achievable throughput is investigated by employing optimal power allocation at transmitters. The proposed model is analysed using real world solar energy harvesting data. The results show that consistently high throughput can be achieved by scheduling the energy arrival duration, while at the same time, energy inefficiency can also handled by optimal power allocation at the devices.

Finding critical nodes in a network is a significant task, highly relevant to network vulnerability and security. We consider the node criticality problem as an algebraic connectivity minimization problem where the objective is to choose nodes which minimize the algebraic connectivity of the resulting network. Previous suboptimal solutions of the problem suffer from the computational complexity associated with the implementation of a maximization consensus algorithm. In this work, we use spectral partitioning concepts introduced by Fiedler, to propose a new suboptimal solution which significantly reduces the implementation complexity. Our approach, combined with recently proposed distributed Fiedler vector calculation algorithms enable each node to decide by itself whether it is a critical node. If a single node is required then the maximization algorithm is applied on a restricted set of nodes within the network. We derive a lower bound for the achievable algebraic connectivity when nodes are removed from the network and we show through simulations that our approach leads to algebraic connectivity values close to this lower bound. Similar behaviour is exhibited by other approaches at the expense, however, of a higher implementation complexity.

Underwater Sensor Networks (USNs) are characterized with high error probability, low bandwidth and large delays due to use of acoustic channels. Typical reliability approaches used in terrestrial sensor networks - retransmissions, multipath transmissions and Forward Error Correction (FEC) - have been employed in underwater sensor networks. However, these schemes have not been analyzed and compared for underwater environments which differ from the terrestrial environment. In this paper, we perform analysis of retransmissions, multipath transmissions and FEC approaches for underwater sensor networks in terms of end-to-end packet delivery probability, delay and overhead. Through analytical models and trace driven simulations, the paper shows that the FEC scheme achieves the target data delivery probability with the minimum delay and overhead as compared to retransmissions and multipath transmissions approaches. We also present extended models for generic reliable data delivery schemes which simultaneously consider multiple paths, retransmissions and forward error corrections. These models are also applicable to networks other than underwater sensor networks.

Critical node discovery plays a vital role in assessing the vulnerability of a network to an abrupt change, such as an adversarial attack or human intervention. In this paper, we propose a new metric to characterize the criticality of a node in an arbitrary network which we refer to as the Combined Banzhaf & Diversity Index (CBDI). The metric utilizes a diversity index which is based on the variability of a node's attributes relative to its neighbors and the Banzhaf Power Index which characterizes the degree of participation of a node in forming shortest paths. The Banzhaf power index is inspired from the theory of voting games in game theory. We evaluate the performance of the new metric using simulations. Our results indicate that in a number of network topologies, the proposed metric outperforms other proposals which have appeared in the literature. The proposed CBDI index chooses more critical nodes which, when removed, degrade network performance to a greater extent than if critical nodes based on other criticality metrics were removed.

Energy provisioning trend in Wireless Sensor Networks (WSNs) is shifted towards alternate sources by utilizing available ambient energy, of which solar irradiance harvesting is considered a viable alternative to fixed batteries. However, the energy storage buffer for harvested solar energy should be adaptive to the sporadic nature of the diurnal solar radiation availability. We believe that the typical fixed battery models no longer apply in harvesting enabled sensors. Therefore, we propose a random walk based stochastic model namely; Trinomial Random Walk (TRW) model for the storage capacity of harvesting enabled sensors. We then apply the proposed model on a comprehensive solar radiation data set of four different locations around the globe. Our performance evaluation demonstrates that the proposed model better analyze the sporadic nature of the diurnal solar radiation availability for estimating the required storage capacity. We further investigate an optimal power consumption value for a given energy store size, such that the utilization of harvested energy is maximized and the probability of energy depletion is minimized. For a given energy harvesting scenario, our model better approximates the optimal load with probability of up to a maximum of 98%, compared to a maximum of 37% for the binomial random walk model.

This paper describes the experience of deploying and operating the LOng-range Ultra-wIdeband Spectrum Sensing (LOUISS) testbed at the School of Electrical Engineering amp; Computer Science (SEECS) of the National University of Sciences amp; Technology (NUST), Islamabad. The LOUISS testbed uses the Agilent N6841A RF sensors and was used to conduct a survey of spectrum occupancy in Islamabad. This paper provides a detailed description of the sensors' architecture as well as the architecture and configuration of the testbed. This is followed by results from an RF survey conducted on NUST's campus using LOUISS as a tool for dynamic spectrum sensing. Sample data sets from this RF survey have been made available online to the research community. Finally, two applications developed on top of this testbed are presented; 1) A desktop application that flags illegal RF spectrum transmitters, tracks spectrum use, and allows interested parties to search for contiguous bands that are available for local use. 2) An Android app that makes geolocation information of transmitters available on mobile devices.