In this work, we study the data collection problem using a data mule in millimeter Wave (mmWave) sensor net-works. The data mule aims to collect data (from a redundant set of sensors) within a minimum expected time, constrained by a lower bound on the delivery probability. Due to multiple factors of uncertainty, such as duty-cycling of sensors and mmWave link attenuation due to random blockage effects, the data mule faces a non-trivial challenge of how many times to probe a sensor (exploit) and which sensor to probe (explore), in order to meet the aforementioned goal. The aforementioned (exploit/explore) problem is modeled using a partially observable Markov decision process. As the direct method of solving the model characterized by a continuous state space is computationally intensive, we present an alternate method by investigating the structural properties of the model. To this end, a simple threshold-based myopic optimal data collection algorithm is shown to exist, and the closed-form expression for the threshold is derived. The effectiveness of the proposed optimal algorithm is validated against state-of-the-art learning-based protocols through extensive simulations.

The Named Data Networks (NDNs) are considered as a suitable architectural paradigm for collecting the sensory data generated by the Internet of Things (IoTs). However, towards retrieving fresh ephemeral sensory data, we show that the naive integration of NDN and IoT/sensor, without using learning-based forwarding methods, is energy-demanding and sub-optimal in retrieving fresh data. To this end, a novel learning-based NDNoT data forwarding framework is proposed. We analytically show that a Reinforcement Learning (RL) based NDN forwarding strategy can optimally retrieve fresh sensory data from IoTs in an energy-efficient manner. The proposed NDN forwarding strategy is modelled as an optimal-stopping problem using Multi-Armed Bandit (MABs). The structural result of the MAB is investigated to yield optimal policy in terms of both energy efficiency and fresh data delivery. The resultant optimal policy implemented in the form of an NDNoT forwarding algorithm namely, FRESH is validated and compared against the state-of-the-art NDN forwarding strategies using extensive simulation.

Device-to-Device (D2D) communication helps in increasing the coverage range and throughput in millimeter Wave (mmWave) networks. The performance of the mmWave D2D communication mainly depends on selecting the best neighbor device through a process of Beamforming Training (BT). The process of BT involves pointing beams of multiple resolutions at different angles to select the best neighbor device in terms of link quality. While the sender exhaustively performs BT with all the neighbors in naive BT, the Reinforcement Learning (RL) based techniques, on the other hand, employ exploration/exploitation strategies to intelligently search for the best neighbor.The state-of-the-art RL algorithms typically yield sub-optimal performance, by trading-off computational tractability over optimality. Tractable optimal solving methods are therefore critical in improving the performance of neighbor selection through an intelligent BT process. To this end, this work studies the problem structure of the mmWave BT neighbor selection process formulated as a Multi-Armed Bandit (MAB) problem and provides a simple and scalable optimal method to select mmWave D2D neighbors with the objective of maximizing throughput performance. With extensive simulations, the efficacy of the proposed framework is demonstrated.

The Internet of Medical Things (IoMT) technology’s fast advancements aided smart healthcare systems to a larger extent. IoMT devices, on the other hand, rely on centralized processing and storage systems because of their limited computational and storage capacity. The reliance is susceptible to a single point of failure (SPoF) and erodes the user control over their medical data. In addition, Cloud models result in communication delays, which slow down the system’s overall reaction time. To overcome these issues a decentralized distributed smart healthcare system is proposed that eliminates the SPoF and third-party control over healthcare data. Additionally, the proposed Fortified-Chain 2.0 uses a blockchain-based selective sharing mechanism with a mutual authentication technique to solve the issues, such as data privacy, security, and trust management in decentralized peer-to-peer healthcare systems. Also, we suggested a hybrid computing paradigm to deal with latency, computational, and storage constraints. A novel distributed machine learning (ML) module named random forest support vector machine (RFSVM) also embedded into the Fortified-Chain 2.0 system to automate patient health monitoring. In the RFSVM module, a random forest (RF) is used to select an optimal set of features from patients data in real-time environment and also support vector machine (SVM) is used to perform the decision making tasks. The proposed Fortified-Chain 2.0 works on a private blockchain-based distributed decentralised storage system (DDSS) that improves the system-level transparency, integrity, and traceability. Fortified-Chain 2.0 outperformed the existing Fortified-Chain in terms of low latency, high throughput, and availability with the help of a mutual authentication method.

The rapid developments in the Internet of Medical Things (IoMT) help the smart healthcare systems to deliver more sophisticated real-time services. At the same time, IoMT also raises many privacy and security issues. Also, the heterogeneous nature of these devices makes it challenging to develop a common security standard solution. Furthermore, the existing cloud-centric IoMT healthcare systems depend on cloud computing for electrical health records (EHR) and medical services, which is not suggestible for a decentralized IoMT healthcare systems. In this article, we have proposed a blockchain-based novel architecture that provides a decentralized EHR and smart-contract-based service automation without compromising with the system security and privacy. In this architecture, we have introduced the hybrid computing paradigm with the blockchain-based distributed data storage system to overcome blockchain-based cloud-centric IoMT healthcare system drawbacks, such as high latency, high storage cost, and single point of failure. A decentralized selective ring-based access control mechanism is introduced along with device authentication and patient records anonymity algorithms to improve the proposed system's security capabilities. We have evaluated the latency and cost effectiveness of data sharing on the proposed system using Blockchain. Also, we conducted a logical system analysis, which reveals that our architecture-based security and privacy mechanisms are capable of fulfilling the requirements of decentralized IoMT smart healthcare systems. Experimental analysis proves that our fortified-chain-based H-CPS needs insignificant storage and has a response time in the order of milliseconds as compared to traditional centralized H-CPS while providing decentralized automated access control, security, and privacy.

Over the past few decades, finding an optimal spatial configuration of localizing sensors has been mainly approached as a single-objective optimization (SOO) problem with a 2-D perspective for indoor designs. This article presents a novel multiobjective optimization (MOO) approach that analyzes the beacon placement problem (BPP) for the three-dimensional coordinate point cloud representation of indoor environments. The present research targets wireless localization scenarios with static obstacles and noisy range measurements. The proposed methodology is an optimization tool chain that explores a set of Pareto-optimal beacon configurations using the nondominated sorting genetic algorithm (NSGA)-II. The derived nondominant solutions are analyzed by simulations for their performance for accuracy and coverage over different indoor designs. Finally, a performance-based ranking system is presented to direct users in achieving a single optimal solution.

Security plays a vital role in Wireless Sensor Networks (WSN) for providing reliability to the network. In WSN, where nodes, in addition to having their inbuilt capability of sensing, processing, and communicating data, also possess certain risks. These risks expose them to attacks and bring in many security challenges. Many researchers are engaged in developing innovative design paradigms to address security issues by developing trust management systems. In WSN, trust is important for the establishment of cooperation among the sensor nodes. The article presents a sociopsychological model for detecting fraudulent nodes in WSN. The three factors, viz. ability, benevolence, and integrity, are used for the computation of trust. Furthermore, the article provides a novel consensus-aware sociopsychological approach to deal even in the presence of higher number of fraudulent nodes than benevolent nodes. The proposed work has been implemented in the LabVIEW platform and extensive simulations were carried out to study its performance. Additionally, it is experimentally evaluated on a testbed of size 16 nodes to obtain results that demonstrate the accuracy and robustness of the proposed model.

Biological Immune Systems have intelligent capabilities of detecting foreign bodies which attack our body. Moreover they have inherent insightful capabilities to remember them, when they hit the body again. Primary response is the initial response instantiated by the body to the attack and secondary response is the response hence forth. Secondary response is naturally faster because of its characteristic of remembering the cure of the attack. Similar type of perspicacious nature can be adapted in removal of fraudulent nodes in wireless sensor network. The work proposes a novel algorithm for the detection and removal of the fraudulent nodes. It first detects the fraudulent nodes by machine learning module and then removes these nodes by immune-inspired module. Eventually if the same type of malicious nature is seen again, analogy of secondary response of immune system is instigated in sensor network. Proposed work has been implemented in LabVIEW platform and obtained results that demonstrate the accuracy and robustness of the proposed model.

The use of traditional TCP, in its present form, for reliable transport over ad hoc wireless networks (AWNs) leads to significant degradation in the network performance, in terms of reduction in average network throughput and increase in packet losses. This is primarily due to the congestion window updation and congestion control mechanisms employed by TCP. TCP follows a deterministic approach for updating the size of the congestion window, which is less suitable for loss-prone AWNs as it leads to very frequent occurrences of congestion in the network. Another reason is TCP invokes the congestion control mechanism for both congestion and wireless losses, as it cannot distinguish between them. Hence, in order to use TCP in AWNs, an efficient mechanism must be provided with TCP for updating the size of the congestion window based on the network conditions and distinguishing the congestion losses from wireless losses. In order to address these problems, we propose Learning TCP, a novel learning automata based reliable transport protocol for AWNs, which efficiently adjusts the size of the congestion window and thus reduces the packet losses. The key idea behind Learning-TCP is that, it dynamically adapts to the changing network conditions by observing the occurrence of events, such as arrival of acknowledgment (ACK) and duplicate ACK (DUPACK) packets and appropriately updates the congestion window size. In addition to this, we use a deterministic approach for packet loss discrimination, in order to take the appropriate action for each type of loss. Learning-TCP, unlike other existing proposals for reliable transport over AWNs, does not require any explicit feedback, such as congestion, link failure, and available bandwidth notifications, from the network. We provide extensive simulation studies of Learning-TCP under varying network conditions that show increased throughput and reduced packet loss compared to that of the traditional TCP.