The emergence of wearable medical devices and wireless body area networks (WBANs) has enabled continuous, real-time patient monitoring. These systems generate large volumes of health data, requiring low-latency and reliable processing for timely interventions. However, local processing is often inefficient due to the energy and computational limitations of mobile devices. Offloading tasks to edge computing and cloud resources offers a promising alternative. Nonetheless, optimizing offloading decisions in dynamic healthcare scenarios remains challenging due to heterogeneous task requirements and varying computational resources. This paper presents a hierarchical actor-critic task offloading approach (HACTO), a deep-reinforcement-learning framework designed to enhance the efficiency and adaptability of task offloading in healthcare scenarios. By introducing a hierarchical decision structure, HACTO reduces complexity and improves learning performance. The problem is modeled as a Markov decision process and solved using the deep deterministic policy gradient algorithm. HACTO jointly optimizes task offloading with respect to three objectives: meeting task deadlines, minimizing the energy consumption of mobile devices, and reducing resource usage costs. Our experimental results show that HACTO outperforms traditional and deep-reinforcement-learning-based offloading strategies, making it a promising solution for intelligent task offloading in resource-constrained WBAN environments.

Cardiovascular disease (CVD) is a leading cause of death globally. The unpredictability and severity of CVDs, such as sudden cardiac arrests, necessitate real-time monitoring and prediction using the Internet of Things (IoT) and artificial intelligence (AI) for timely intervention. Existing AI models and IoT frameworks for CVD prediction often lack integration of diverse data and fail to provide transparency in predictions, reducing user confidence and treatment effectiveness. We propose an explainable-IoT framework leveraging eXplainable AI (XAI) in the cloud layer and a mobile application to swiftly communicate predictions and explainability to patients, healthcare providers, and other users, facilitating proactive management and informed decisions. The framework integrates sensor data with cloud-based medical records to improve cardiovascular care and build user trust. Using support vector machine (SVM), random forest (RF), k-nearest neighbor (KNN), and deep neural network (DNN), we develop and evaluate CVD prediction models on two datasets: a heart disease dataset (D1) and the Cleveland dataset (D3). Additionally, we use a synthetic dataset (D2) for comparative analysis. The models are evaluated based on accuracy, precision, recall, area under the curve, time, and F1 score. For D3, SVM achieved the best accuracy (84.62%), while RF performed best on D1 and D2 (92.44% and 98.06%, respectively), comparable to state-of-the-art works. Our results highlight the importance of underrepresented physiological features in CVD datasets and the need for comprehensive datasets to enhance CVD model development. Furthermore, while synthetic data (D2) is effective for initial modeling, it requires validation with real-world data for reliable CVD prediction. • Developed an AI-IoT framework for real-time CVD prediction using diverse datasets. • Compared real and synthetic datasets, emphasizing physiological features for CVD accuracy. • Integrated a mobile app for real-time data and improved CVD prevention and management.

The convergence of artificial intelligence (AI) and the space industry is a crucial step towards advancing humanity. It has the potential to create innovative technologies and services, revolutionize existing practices, and provide valuable insights to space researchers. Many private investors and government organizations possess high demand for satellite images for various reasons, including weather forecasting, global disaster management (e.g., floods, earthquakes, and tsunamis), space object location, land records, geolocations, and international shipment management. However, the traditional centralized economic model for space business is costly to implement in terms of infrastructure, deployment, security risks, ownership, and marketing of the data. Additionally, the initial acquisition of satellite images is usually retrieved in a raw format without any authentication or interpretation. Consequently, the integration of AI and the space industry has the potential to revolutionize the existing model by unlocking new avenues to monetize satellite data by introducing automated processes that are more secure and cost-effective. In this paper, we aim to use deep learning models that are capable of automatically analyzing and classifying raw satellite images into valuable and actionable formats. Furthermore, we are inspired to design a blockchain-based solution to make satellite image datasets available to private and government agencies through a user-friendly and secure marketplace. The proposed solution will focus on a decentralized socio-economic model using blockchain to increase the security of transactions. Blockchain networks facilitate the fair exchange of transactions using smart contracts that will execute and enforce agreement rules among the blockchain participants. The proposed research will mainly focus on the efficient design of smart contracts for satellite data blockchain. In addition, our integrated image quality assessment method is built and tested to accurately group high-quality satellite images into valid categories to make the trading and marketing process more efficient.

Wireless Body Area Network (WBAN) consists of health and environmental sensing devices and a dedicated hub node. The hub node, which has more processing power and capabilities, collects and forwards sensed data from the sensing devices to a backend server for further processing and actions. Sensing devices use the CSMA/CA mechanism to communicate with the hub. This access mechanism may cause frame retransmission of corrupted frames due to collisions resulting in performance degradation. The IEEE 802.15.6 standard specifies seven different user priorities for different traffic types. Nodes with higher user priorities have a small channel contention window to access the channel more frequently, causing nodes with lower user priorities to starve. To mitigate this problem, we propose a mechanism based on priority switching. In the proposed mechanism, the nodes with a higher priority switch their contention window to that of the lowest user priority and then switch back to their original contention window after sending a single successful packet. We conducted intensive simulations to validate the proposed mechanism, which shows significant improvement in the packet delivery rate of lower user priority nodes and all the nodes’ overall performance in terms of throughput, energy consumption, and inter-frame delay.

Wireless Body Area Network (WBAN) consists of health and environmental sensing nodes and a dedicated hub. The hub has more processing power and capabilities than the sensor nodes. It collects sensing data and forwards them to corresponding destinations for further processing and actions. In case of emergencies, the hub delivers the received data to the emergency department to take quick action. The end-to-end link between the sensor node and the emergency department mainly depends on the sensor node's bottleneck link and the hub. WBAN nodes communicate with the hub by accessing the physical communication channel in a randomly distributed fashion. Random channel access may cause retransmission of corrupted frames due to frame collisions. This causes performance degradation of the deployed WBAN. The IEEE 802.15.6 standard specified eight different user priorities for different traffic types. Among these priorities, priority seven is dedicated to reporting emergency and medical implant events. If several nodes report at this priority, this causes lower priority nodes to starve. In this work, we allow nodes to switch from lower priorities to priority seven in emergencies. We conducted several experiments to study the effects of priority seven on other nodes in normal operations and emergencies. The study results showed that the packet delivery rate is severely affected when some nodes switch from normal user priority to priority seven. More studies and investigations need to be conducted to determine the optimum operation of WBAN, especially in the case of an emergency.

Connected healthcare is the process of integrating healthcare smart applications into smart devices. These systems can enable better patient-hospital experience, efficient time usage, reduced errors, safety and security, and ultimately improved treatments. These smart devices which form an IoT network are extremely dynamic because of the user movement. In an environment where there is a constant change in the network topology and its traffic profile, it is a challenging task to provide reliable network connectivity and to maintain the IoT network. Therefore, ensuring healthcare traffic is resilient towards change in the traffic profile is of paramount importance. This paper leverages graph theory concepts to understand the behaviour of the healthcare IoT network. The paper highlights the importance of the PN (PN) and traffic splitting (stratification). A PN is a node in the network which has enough computing resources to share with other network devices. By optimizing the selection of PN, the drastic improvement in the network performance could be achieved. Moreover, we show that splitting the traffic along with optimized PN selection minimizes the chances of healthcare traffic drop during the period of high network usage.

Scientific research in the area of human genomics is no longer restricted to scientists. With the introduction of companies such as 23andme and Ancestry, the interested people of the public have been getting involved in genetic studies. However, the nature of the data being collected and its link to confidential, personal information will need to be properly secured and complete to prevent the same issues faced by electronic medical records in healthcare: fragmentation and risk of disclosure. By integrating blockchains with genomics and healthcare, patients and research participants will be able to participate in their own healthcare and research, increasing patient and user centricity. In this paper, a layered architecture of how blockchains can be implemented in genomics and healthcare is proposed. The proposed architecture shows how communication between users, researchers, and medical professionals can be improved while encouraging genetic research while improving security and privacy.

Spectrum sensing in cognitive radio is a crucial task for detecting spectrum opportunities. Due to the intense demand for the frequency spectrum, average spectrum occupancy has increased, resulting in high spectrum scarcity. Under these circumstances, spectrum sensing needs to be performed on larger portions of the frequency spectrum, up to several GHz, to better explore spectrum opportunities. This leads to wideband spectrum sensing. In this paper, wideband spectrum sensing using wavelet-based detection is investigated, and a system model using collaborative wavelet-based detection is proposed. The performance of this edge detection model is investigated using three fusion rules: OR, AND and Majority.

Due to the expansion of small scale distributed energy resources such as solar rooftop and PEVs, decentralized energy trading is expected to be one of the most important components of next generation power systems. Decentralized peer-to-peer energy exchange systems are believed to be a promising solution not only to solve the scalability and mobility problems in the existing system but also to create a competitive market that can benefit small scale prosumers and consumers. However, such systems need to be equipped with the necessary security, privacy and payment transaction mechanisms to be efficient and trusted. In this paper, we propose a decentralized energy exchange system based on Blockchains. Blockchains is a technology that supports secure transactions between participating entities in a decentralized peer-to-peer network. The proposed system allows for energy exchange between prosumers using a form of smart electronic contracts which are based on Blockchains.

Internet of things (IoT) aims to connect billions of devices to the Internet. These devices generate an enormous amount of traffic. A Wireless Body Area Network (WBAN) aggregates the data generated from the body sensors deployed on the human body or inside it. Due to resource constraints of the deployed sensors, aggregated data is forwarded to a server at an edge node or a cloud for further processing and decision making. With the wide acceptance of IoT deployments, WBANs can also be integrated with IoT to provide a clear contextual environment for smart healthcare applications' users. Software Defined Networks (SDN) is a new networking paradigm providing centralized control and programmability of the networks. These features of SDN open new vistas for WBAN and IoT applications. In this paper, a personal digital assistant (PDA) is configured as an SDN switch, and we emulate the SDN functionality with Mininet to verify that this approach can be used in WBANs with minimum deployment complexity and network overhead, while efficiently manage the connected devices and the connections between them.

Modern information technology services largely depend on cloud infrastructures to provide their services. These cloud infrastructures are built on top of Datacenter Networks (DCNs) constructed with high-speed links, fast switching gear, and redundancy to offer better flexibility and resiliency. In this environment, network traffic includes long-lived (elephant) and short-lived (mice) flows with partitioned/aggregated traffic patterns. Although SDN-based approaches can efficiently allocate networking resources for such flows, the overhead due to network reconfiguration can be significant. With limited capacity of Ternary Content-Addressable Memory (TCAM) deployed in an OpenFlow enabled switch, it is crucial to determine which forwarding rules should remain in the flow table and which rules should be processed by the SDN controller in case of a table-miss on the SDN switch. This is needed in order to obtain the flow entries that satisfy the goal of reducing the long-term control plane overhead introduced between the controller and the switches. To achieve this goal, we propose a machine learning technique that utilizes two variations of Reinforcement Learning (RL) algorithms—the first of which is a traditional RL-based algorithm, while the other is deep reinforcement learning-based. Emulation results using the RL algorithm show around 60% improvement in reducing the long-term control plane overhead and around 14% improvement in the table-hit ratio compared to the Multiple Bloom Filters (MBF) method, given a fixed size flow table of 4KB.

Users of plug-in-electric vehicles (PEV) often need to charge outside their home power grid networks i.e. outside their supplier. This basically constitutes a roaming charging case. Hence, users should be protected from possible security and privacy attacks. Moreover, remote authorization and payment transaction mechanism is required between the different power suppliers. In this paper, we propose a secure charging and payment transaction protocol for roaming PEV charging. Our protocol is developed based on the principles of the Secure Electronic Transaction (SET) protocol. The protocol protects user's privacy not only from external suppliers but also from their own suppliers. Moreover, our protocol is designed in such a way that it is robust against the known security attacks. Our proposed work provides anonymous authorization and payment simultaneously not only between the roaming user and an external supplier but also between suppliers.

Plug-in electrical vehicles (PEVs) are becoming very popular among the car manufacturing industry and among researchers. This is due to their multiple benefits which come from their ability to charge and discharge power from and to the power grid. This can help reduce CO2emission and can be a great source of stabilization to the power system through delivering ancillary services such as spinning reserve, peak power, voltage and frequency regulations. However, this rapidly increasing industry has also introduced various kinds of security and privacy concerns that need to be addressed. Among these security issues is the lack of access control system which can manage who/is able to charge/discharge these types of vehicles particularly in systems where PEVs are driven by multiple drivers such as in fleet management and car renting company. In this paper, we propose architecture, called UConPEV, for managing PEV charging/discharging and controlling the usage of PEVs not only before an operation, but also during and after. We utilize the notion of usage control described in the well-known Usage Control model (UCON).

In this paper, we propose an end-to-end smart electric vehicle charging infrastructure system architecture with an online scheduling scheme. The online scheduler is an important component of the architecture to manage resources at the charging stations. The scheduler operates in two steps. The first step is decentralized and is executed by each vehicle requiring battery recharge. The other step is performed by the central controller, a component of the architecture, to schedule vehicles for recharge based on users' requirements. We provision the scheduler with two plans, namely, plan A and plan B. In plan A, the scheduler tries to admit the requests for an immediate charge. If this plan fails, the scheduler applies plan B where it admits requests for delayed charging within a look-ahead time. The simulation was conducted to test the performance of the scheduler and its algorithm with and without look-ahead scheduling.

This paper addresses the spectrum utilization due to cognitive radio (CR) access in terms of the secondary users' network capacity in Nakagami fading channels. A new mathematical model is developed for the secondary network capacity when both accessing and sensing channels undergo Nakagami-m fading with none-integer fading parameters. Exact and approximate analytical expressions for the capacity are derived. Some simplified special cases are also investigated such as the integer-fading parameter. The numerical evaluation of channel capacity is presented for different channel sensing and accessing methods under different fading scenarios. The effects of detection and accessing channels' parameters on the capacity are also explored. It is found that a good sensing system with high secondary user signal-to-noise ratio, accessing the licensed band maximizes the spectrum utilization. To maximize the spectrum utilization a proper sensing scheme is required given that an acceptable QoS of primary users is maintained.

The smart grid will increase building energy efficiency and conservation via combining information and communication technologies, advanced instrumentations, system intelligence, and information on the end user. Specifically Demand Side Management (DSM) programs serve as an aid in energy conservation and management strategies as well as in the collection of real-time consumption information data. As proposed in this paper, real-time, fine-grain consumption data at the point of use in buildings can be used by building energy managers, utilities, and the end user for planning, load forecasting, and feedback for providing information that may lead to end user behavior change. This paper focuses on the development of a platform detecting every active node in the building remotely and using a combination of tools to monitor and locate these active nodes. The paper will discuss the main component of this platform, the energy node locator and how it works based on the principles of Time Domain Reflectometry (TDR). Preliminary results will be presented and used to evaluate the node locator principles. Summary and future work will be presented to discuss the framework moving forward.

The need for efficient optimization of network resources to prolong the life time of large-scale and dense deployments of Wireless Sensor Networks opened the door for researchers to study and produce efficient clustering techniques. Clustering has been proven to be an effective approach for organizing a large scale WSN into connected groups increasing the life time and the reliability of such networks. Distance of the nodes from the base station and inter-node distances can have a high influence on saving energy and prolonging the network life time. In this paper, a new algorithm based on node ranking is proposed. The proposed algorithm uses node ranking to elect cluster heads based on energy levels and positions of the nodes in reference to the base station used as a sink for gathered information. Also the algorithm provides a stronger coverage through maintaining a global knowledge at the base station which in turn ensures that all nodes are reachable through connected cluster heads. Since the BS calculates the number of rounds a cluster head can remain through in advance, this feature will have a great effect in prolonging the network life time as it reduces the amount of energy wasted on replacing cluster heads. The performance of this algorithm compared to other well-known algorithms will be presented.

The denseness and random distribution of large-scale WSNs makes it quite difficult to replace or recharge nodes. Energy efficiency and management is a major design goal in these networks. In this paper, a new node energy efficient algorithm using energy thresholds to replace cluster heads is proposed. The proposed algorithm uses node ranking to elect cluster heads based on energy levels and positions of the nodes in reference to the base station (BS) which is used as a sink for gathered information. Since the BS calculates the number of rounds a cluster head can remain through in advance, this feature will have a great effect in prolonging the network life time as it reduces the amount of energy wasted on replacing cluster heads. In addition, hybrid redundant nodes duty-cycle is used for nodes to take turn in covering the monitored area which will improve the performance further. The performance of this algorithm compared to other well-known algorithms is shown. Also the performance of the algorithm using random cluster heads replacement and using threshold values to replace the cluster heads are compared.

Assessment of curriculum outcomes represents a critical stage of any degree program evaluation to support continuous academic improvement. The collection, aggregation and analysis of assessment data are notoriously complex and time-consuming processes. Several actors are involved across the assessment workflows, which we propose to streamline in the context of a portal-based approach. We first describe a model for capturing and propagating assessment data across the assessment cycle as well as tracking potential deficiencies in the assessed program. We then describe our portal-based implementation of the proposed model in the context of an ACM-based IT Curriculum program which assessment processes respond to ABET accreditation requirements. We reveal the results of our assessment experience using the proposed model and the corresponding in-house developed portal. We show the corrective curricular actions facilitated by our assessment tool to close the assessment cycle.

Voice over IP (VOIP) is an application of great importance, particularly because of the significant revenue it can generate. In order for the Internet to constitute an attractive alternative to the traditional Public Switched Telephone Network (PSTN), it must provide high quality VOIP services. Given the importance of VOIP today, a new course was introduced as part of the undergraduate curriculum for the Information Technology students with specialization in Networking. The main focus of this course is on VOIP protocols and requirements. Laboratory experiments were designed so that students can obtain needed hands on experience to better realize the concepts being taught. This paper will provide details on the VOIP course material, structure with a focus on the designed hands on experiments the tools used, and assessment process.

The emergence of low-cost low-power wireless sensor devices has increased the demand of using wireless sensor networks in various applications. Being wireless and mobile, sensor devices have yet facilitated the global demand on using sensors for tracking applications. Using wireless sensor networks in highly populated indoor areas will save energy, improve security, and lessen event response time. In this research work, we investigate the deployment of wireless sensor networks in a highly dynamic WLAN environment and a highly populated indoor area. We conducted two sets of experiments. In the first set of experiments, the operating channel of wireless sensors did not coincide with the WLAN channel, i.e. interference was minimized and for the second set of experiments, both wireless channels coincided within the same spectrum. We monitored the network performance in the two experimental setups in terms of delivery rate and stability.

Video smoothing techniques can be used to facilitate more effective transmission and to preserve better video quality. In this paper we develop a semi-optimal video smoothing approach to manage the transmission rates of MPEG-4 and H.264 video over a QoS-based wireless LTE network. The proposed technique utilizes a smoothing buffer with pre-defined thresholds to smooth the transmission rates while assuming minimal information about the video to be transmitted. The results obtained showed a significant improvements in smoothing transmission rate variability. In addition, we show a model for the wireless LTE channel and use it as a feedback to manage smoothing and regulate and map the transmission rates based on the availability of network resources.

3GPP Long Term Evolution (LTE) broadband mobile wireless network has been put in place to support diverse IP based traffic such as voice, data and multimedia. In order to support the various integrated services and the ever growing multimedia applications with guaranteed quality of service (QoS), a resource provisioning mechanism is becoming a major demand. Call Admission Control (CAC) is one of the main resource management components that limit the number of connections to the system capacity and guarantee the QoS of ingoing calls. In this paper, we propose a novel CAC algorithm that considers admission of calls for future startup. We consider also the immediate and handoff calls with certain priority admission scheme. We then use a stochastic discrete-event simulator to evaluate the performance of the CAC algorithm. The results obtained for look-ahead admission are compelling and indicate the usefulness of making advance resource reservations.

A lot of work has been done to evaluate the real time applications’ performance over wireless networks. Some work focused on delay as a performance metric. However, no work studied the delay random behavior over a test-bed which imitates as much as possible actual operative networks. In this paper, we designed and run different experiments over heterogeneous 802.11 Wireless Mesh Network (WMN) test-bed with a scope of studying the delay probability distribution over multihop WMN. We anticipate that by knowing the link delay distribution, we will be able to use this information to design sound algorithms to provide for QoS. For example, designing a partitioning algorithm capable of partitioning multiple end-to-end QoS requirements into link QoS requirements. We designed different experiments to measure the link and end-to-end delays over the WMN test-bed. The measured link delays are used to construct an empirical histogram. Our experiments reveal that irrespective of the number of hops along the paths and type of traffic crossing the link, the empirical histograms almost have same general shapes. The empirical histograms are fitted into different types of standard pdf distributions. We found that the best fits for almost 90% of the empirical distributions are two standard distributions; gamma and logistic.

Local area network (LAN) security is a critical and mandatory element that network administrators must master. It is often thought of network security as protecting the network from external attacks and intrusions. However, internal attacks can also be as damaging and malicious as external ones. One of the well known attacks in networking is packet spoofing at the different network layers. This paper discusses how spoofed ARP packets can be used by malicious users to redirect and use network's traffic to launch an attack against users' hosts. Limitations of current intrusion detection systems (IDSs) in detecting traffic redirection attacks are also discussed. The paper then proposes practical and efficient mechanisms for detecting such malicious attacks in a switched LAN environment. In addition, the effect of the proposed techniques on network performance is shown to be minimal given the gained benefits.

E-learning is an emerging approach to education reflected in new learning techniques such as active and cooperative learning. Supporting communication technologies such as web-based tools, mobile devises and smart classroom used for interactive learning often come with side-effects that can be disruptive to the normal flow of the learning and the teaching processes. The use of technology van be hindered by the learning curve associated with the technology used during the class, the distraction from the initial goals of the course, the risk of mixing-up material related to the course, and concepts associated with the tools used in class, and finally, the time and effort overhead required from participants in preparing and using these tools. Therefore, it becomes crucial to continue benefiting from the advances in cooperative and communication tools, and in the same time minimizing the incurred overhead. In this paper, we propose a new classroom assessment tool: Active Class Probe (ACP) which can be used in an active and cooperative environment. ACP's main goal is designed such as to maintain focus on the initial course's learning goals, and minimize the external distraction incurred by its use in the classroom.

This paper discusses issues related to the delivery of MPEG-4 video over the Internet and wireless channels. MPEG-4's built-in error resilience capabilities such as flexible re-synchronization markers, data partitioning, header protection, reversible VLCs, and forced intra-frame refresh are described. Methods for using these techniques to build a “smart” network decoder are discussed, and the decoder's video quality is measured for various channel error conditions. The effectiveness and overheads of the various error resilience techniques are compared using both peak signal-to-noise ratio measurements and expert viewing. The use of forward error correcting strategies and the effects of packet sizes and boundaries on video quality are also examined.

Through an intensive experimental study, the characteristics of cell delay variation (CDV) and CTD in an ATM network transporting constant bit rate (CBR) traffic under diverse operating conditions are evaluated. We examine the effect of changing network parameters, such as the number of nodes and background load conditions, on the performance parameters of both CDV and CTD. Our results revealed the distributions of CTD and CDV could not be fit to any of the standard distributions (normal, gamma, etc.). The results also present, the autocorrelation behavior of CTD as a function of both load and number of travelled hops. A verification study of the square root law (SRL) of the ATM Forum, on estimating the CTD standard deviation with increasing number of network nodes is also presented. Our results indicate that the SRL reveal very close estimates only under the assumption of a uniform load conditions across all nodes in the network.