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Healthcare monitoring systems have been one of the most important system in the last decade, and they have become increasingly technological. Humans are facing various issue and untimely death due to multiple illness, due to a lack of medical treatment for patients. To overcome this issue a real time health monitoring system has proposed based on IOT. The proposed system consists of mobile application and GSM for continuous wireless monitoring of patients. The main aim is to create a dependable patient management system based on IoT so that healthcare professionals can monitor their patients who are either hospitalized or at home using an IOT based integrated healthcare system to ensure quality patient care. Sensors are used to track vital parameters, and the data collected by the sensors is sent to the cloud via a WiFi module. A wireless healthcare monitoring system has created that can provide real time online information about a patients conditions. The system is made up of sensors, a data acquisition unit, a microcontroller (i.e. ESP32) and software. The patients temperature, heart beat rate, ECG, blood pressure and SPO2 are regularly monitored, displayed and stored by the system and the same has been sent to the doctors mobile containing the application. Additionally a message is sent to the doctors mobile if any of the parameter crosses the threshold value. Thus, IOT based real time health monitoring system systematically monitor the condition of patients health and save their life on time.

In the wideband regime, the performance of many of the popular modulation schemes such as code division multiple access and orthogonal frequency division multiplexing falls quickly without channel state information. Obtaining the amount of channel information required for these techniques to work is costly and difficult, which suggests the need for schemes which can perform well without channel state information. In this work, we present one such scheme, called wideband time frequency coding, which achieves rates on the order of the additive white Gaussian noise capacity without requiring any channel state information. Wideband time frequency coding combines impulsive frequency shift keying with pulse position modulation, which allows for information to be encoded in both the transmitted frequency and the transmission time period. On the detection side, we propose a non-coherent decoder based on a square-law detector, akin to the optimal decoder for frequency shift keying based signals. The impacts of various parameters on the symbol error probability and capacity of wideband time frequency coding are investigated, and the results show that it is robust to shadowing and highly fading channels. When compared to other modulation schemes such as code division multiple access, orthogonal frequency division multiplexing, pulse position modulation, and impulsive frequency shift keying without channel state information, wideband time frequency coding achieves higher rates in the wideband regime, and performs comparably in smaller bandwidths.

With the rapid development of 5G, Mobile Edge Computing (MEC) paradigm has emerged to enable various devices or servers at the network edge to contribute their computing capacity for reducing communication delay. A fundamental problem is to preserve satisfactory quality-of-service (QoS) for mobile users in light of densely dispersed wireless communication environment and capacity-constrained MEC nodes. Such user-perceived QoS, typically in terms of the end-to-end delay, is highly vulnerable to both access network bottleneck and communication delay. Previous works primarily focused on optimizing the communication delay through dynamic service placement, while ignoring the critical effect of access network selection on the access delay. This paper studies the problem of jointly optimizing the access network selection and service placement for MEC, with the objective of improving QoS in a cost-efficient manner by judiciously balancing the access delay, communication delay, and service switching cost. We propose an efficient online framework to decompose this long-term time-varying optimization problem into a series of one-shot subproblems. To address the NP-hardness of the one-shot problem, we design a computationally-efficient two-phase algorithm, which achieves a near-optimal solution. Both theoretical analysis on the optimality gap and trace-driven simulations are conducted to validate the efficacy of our proposed solution.

Resource reservation is an essential step to enable wireless data networks to support a wide range of user demands. In this paper, we consider the problem of joint resource reservation in the backhaul and Radio Access Network (RAN) based on the statistics of user demands and channel states, and also network availability. The goal is to maximize the sum of expected traffic flow rates, subject to link and access point budget constraints, while minimizing the expected outage of downlinks. The formulated problem turns out to be non-convex and difficult to solve to global optimality. We propose an efficient Block Coordinate Descent (BCD) algorithm to approximately solve the problem. The proposed BCD algorithm optimizes the link capacity reservation in the backhaul using a novel multipath routing algorithm that decomposes the problem down to link-level and parallelizes the computation across backhaul links, while the reservation of transmission resources in RAN is carried out via a novel scalable and distributed algorithm based on Block Successive Upper-bound Minimization (BSUM). We prove that the proposed BCD algorithm converges to a Karush-Kuhn-Tucker solution. Simulation results verify the efficiency and the efficacy of our BCD approach against two heuristic algorithms.

Software updates are highly significant in autonomous vehicles. These updates are utilized to provide enhanced features and updated security mechanisms. In order to ensure scalability and smooth roll-out Over-the-air (OTA) mechanism is a preferred option to propagate a software update. However, this approach is vulnerable to security attacks because of existence of wireless communication channel between the vehicle and the manufacturer. In that, an attacker can replace the legitimate software with a malicious software with an intent to get control over the vehicle. In this work, we are motivated to address this problem. We develop an enhanced uptane framework for detection of malicious OTA software updates in autonomous vehicles. For enhancing security, we incorporate convolutional neural network (CNN) in the uptane framework. The proposed framework is able to distinguish between malicious and benign software executables with high accuracy. For training and testing, we create two datasets by collecting executables of Windows and Linux operating system. We encourage the use of transfer learning by exploiting the developed CNN models in order to detect malicious executable designed for autonomous vehicles. We also benchmark the CNN models against state-of-the art models. Our work is highly beneficial for the community in providing a secure mechanism for software updates.

Underwater wireless acoustic networks (UWANs) take advantage of acoustic communication to enable many distinct applications. The peculiar features of underwater acoustic channels, such as long propagation delay, high bit error rates, and severely limited bandwidth, make the use of medium access control (MAC) solutions designed for terrestrial radio networks inefficient in UWANs. This paper proposes UW-SEEDEX, a MAC protocol for UWANs that employs random time slot schedules, created from seeds, to avoid collisions. After exchanging seeds, nodes can know other's entire schedules, allowing them to then better plan their transmissions. Simulations evaluate how each of UW-SEEDEX's parameter affects its performance in metrics such as end-to-end delay, energy consumption, transmissions per data reception, and reception rate using different test scenarios. Simulations also show that UW-SEEDEX can perform better than other MAC solutions, delivering more messages than protocols such as UW-Aloha and Slotted FAMA and reduced, on average, up to three times the number of transmissions required for each message reception in networks with grid topologies. UW-SEEDEX presented reception rates close to 100%, low energy cost, and fewer transmissions per data reception. Our code is available at https://gitlab.com/epmcj/ns-3-dev/-/tree/new-uan-mac-protocols.

Unmanned Aerial Vehicles (UAVs) have received a wide range of attention for military and commercial applications. Enhanced with communication capability, UAVs are considered to play important roles in the Sixth Generation (6G) networks due to their low cost and flexible deployment. 6G is supposed to be an all-coverage network to provide ubiquitous connections for space, air, ground and underwater. UAVs are able to provide air-borne wireless coverage flexibly, serving as aerial base stations for ground users, as relays to connect isolated nodes, or as mobile users in cellular networks. However, the onboard energy of small UAVs is extremely limited. Thus, UAVs can be only deployed to establish wireless links temporarily. Prolonging the lifetime and developing green UAV communication with low power consumption becomes a critical challenge. In this article, a comprehensive survey on green UAV communications for 6G is carried out. Specifically, the typical UAVs and their energy consumption models are introduced. Then, the typical trends of green UAV communications are provided. In addition, the typical applications of UAVs and their green designs are discussed. Finally, several promising techniques and open research issues are also pointed out.

Wireless Sensors Networks (WSN) is the self-configured wireless network which consists of a huge measure of resource-restrained Sensor Nodes (SN). In WSN, the key parameters are effectual energy utilization and security. The adversary could send false information because of the Malicious Nodes' (MNs') presence. Thus, to shun security threats, it is vital to find and isolate those MN. Consequently, this work proffered a solution for detecting MN in WSN utilizing SN's parameters. This work not only regards the security but also rendered energy-efficient data transmission by means of choosing the Cluster Head (CH) centered on the sensor's residual energy. The Improved Deep Convolutional Neural Network (IDCNN) identifies the MN and then isolates them into the malicious list box in the Malicious Nodes Detection (MND) phase. In the energy-efficient DT phase, the EKM algorithm clusters the Trusted Nodes (TN), and, the t-DSBO algorithm selects an individual CH for each cluster centered on those nodes' residual energy. The t-Distribution based Satin Bowerbird Optimization (t-DSBO) selects an alternate CH if the current CH loses its energy. The proposed techniques effectively detect the MN and render energy-efficient DT, which is experimentally proved by comparing it with existing techniques.

Terrestrial and satellite communication networks often rely on two-hop wireless architectures with an access channel followed by backhaul links. Examples include Cloud-Radio Access Networks (C-RAN) and Low-Earth Orbit (LEO) satellite systems. Furthermore, communication services characterized by the coexistence of heterogeneous requirements are emerging as key use cases. This paper studies the performance of critical service (CS) and non-critical service (NCS) for Internet of Things (IoT) systems sharing a grant-free channel consisting of radio access and backhaul segments. On the radio access segment, IoT devices send packets to a set of non-cooperative access points (APs) using slotted ALOHA (SA). The APs then forward correctly received messages to a base station over a shared wireless backhaul segment adopting SA. We study first a simplified erasure channel model, which is well suited for satellite applications. Then, in order to account for terrestrial scenarios, the impact of fading is considered. Among the main conclusions, we show that orthogonal inter-service resource allocation is generally preferred for NCS devices, while non-orthogonal protocols can improve the throughput and packet success rate of CS devices for both terrestrial and satellite scenarios., Comment: arXiv admin note: text overlap with arXiv:1909.10283

Smart body area networks (SmartBAN) is an emerging wireless body area networks (WBAN) standard proposed by the European Telecommunications Standards Institute (ETSI). This paper first examines the potential of SmartBAN medium access control (MAC) layer with scheduled access to support a myriad of WBAN applications, having diverse data rate requirements. Extra scheduled access slots can be allocated to high date rate sensor nodes for managing their data rate requirements. High data rate sensor nodes can also be re-assigned to use the available time slots of low data rate sensor nodes in Inter-Beacon Interval (IBI) by the central hub. But these two schemes incorporate different physical (PHY) and MAC layer overheads related to frame transmission, frame acknowledgement and slot re-assignment. This redundant overhead transmission results in high overhead energy consumption and reduced effective throughput. Therefore, an innovative and flexible enhanced throughput and reduced overhead (FETRO) MAC protocol for scheduled access is proposed in this article. In the proposed scheme, the sensor node data rate requirements are considered while assigning the scheduled access slot duration by allowing minimal changes in the base-line standard implementation. This infers the provision of scheduled access slots with variable slot durations within an IBI. We also evaluate the existing techniques of extra slot allocation and slot re-assignment in SmartBAN as well as the proposed FETRO MAC protocol with variable slot length. The proposed FETRO MAC scheme results in optimizing both the overall throughput and normalized overhead energy consumption per kilo bits per second (Kbps). Additionally, the impact of various WBAN channel models over these throughput management approaches is also investigated. The proposed FETRO MAC protocol with variable slot duration gives an average reduction of 65.5% and 59.16%, respectively, in the hub and nodes normalized overhead energy consumption per Kbps outcomes, as compared to the de-facto SmartBAN MAC scheduling strategies.

Short-range wireless technologies will enable vehicles to communicate and coordinate their actions, thus improving people's safety and traffic efficiency. Whereas IEEE 802.11p (and related standards) had been the only practical solution for years, in 2016 a new option was introduced with Release of 14 long-term evolution (LTE), which includes new features to enable direct vehicle-to-vehicle (V2V) communications. LTE-V2V promises a more efficient use of the channel compared to IEEE 802.11p, thanks to an improved PHY layer and the use of orthogonal resources at the MAC layer. In LTE-V2V, a key role is played by the resource allocation algorithm and increasing efforts are being made to design new solutions to optimize the spatial reuse. In this context, an important aspect still little studied, is therefore that of identifying references that allow, first, to have a perception of the space in which the resource allocation algorithms move and, second, to verify the performance of new proposals. In this paper, we focus on a highway scenario and identify two algorithms to be used as a minimum and maximum reference in terms of the packet reception probability (PRP). The PRP is derived as a function of various parameters that describe the scenario and settings, from the application to the physical layer. Results, obtained both in a simplified Poisson point process scenario and with realistic traffic traces, show that the PRP varies considerably with different algorithms and that there is room for the improvement of current solutions.

The increase of cyberattacks by using the Internet of Things devices has exposed multiple vulnerabilities not only on devices but also on IoT infrastructures. Now, small devices can affect different networks and their services. This paper presents a review of the vulnerabilities on wireless technologies that gives connectivity to IoT, and the experiences using Software Defined Radio SDR for implementing wireless attacks to IoT technologies are assessed. A systematic literature review was conducted. The types of vulnerabilities and attacks that can affect the wireless technologies that stand the IoT ecosystem and SDR platforms were compared, and recent methods to overcome these attacks were identified. On the IoT reference model, the perception layer was identified as the most vulnerable. Most attacks at this level occur due to hardware limitations, physical device exposure, and heterogeneity of technologies. Future cybersecurity systems based on SDR technologies have notable advantages due to their flexibility and adaptability to new communication technologies and their potential to develop advanced tools. However, the IoT’s cybersecurity challenges are so complex that merging SDR hardware with cognitive and intelligent techniques is highly advisable. Some of these techniques could include deep learning to adapt mitigation systems to rapid technological changes.

This paper focuses on designing high-data-rate wireless communications for drone networks in the mmWave and THz frequency bands. MmWave/THz-band communications has been envisioned as a key technology to achieve ultra broadband wireless links through beamforming in 5G and beyond networks. However, a main challenge with these frequency bands is that the narrow-beam directional wireless links can be easily disconnected because of the beam misalignment in mobile environments. To address this challenge, in this paper we design a new beam control scheme called LeBeam, with the objective of maximizing the expected capacity of the mmWave/THz-band links by determining the optimal beamwidth dynamically under drone mobility uncertainties. In LeBeam, an ESN is adopted to capture the mobility uncertainties of the drones dynamically and predict the optimal beamwidth based on the first- and second-order moments of the drone mobility. The ESN has been trained based on real drone flight traces. The performance of LeBeam is evaluated over UBSim, a newly developed trace-driven Universal Broadband Simulator for integrated aerial and ground wireless networking.

The contemporary wireless sensor applications employ a Heterogeneous Wireless Sensor Network (HeWSN) to achieve its multi-objective missions. Modern wireless nodes constituting the heterogeneous WSN are more versatile in terms of its capabilities, functionalities, and applications. Assigning tasks in a dynamic HeWSN environment are challenging due to its inherent heterogeneous properties and capabilities. The investigation of existing task assignment algorithms reveals (i) the majority of the existing task assignment algorithms were designed for the homogeneous environment, (ii) most of the nature-inspired algorithms were built for centralized architecture. Scheduling tasks by existing task assignment algorithms lead to underutilization of resources as well as to the rapid depletion of network resources. To this end, a novel, distributed, heterogeneous task assignment algorithm adhering the modern sensors capabilities, functionalities and sensor application to attain sustainable computing is required. Based on the investigation, Tissue P-System inspired task assignment algorithm for the distributed heterogeneous WSN has been modelled. The experimental analyses of the proposed method have been self-evaluated as well as compared with the corresponding recent benchmark algorithms under various conditions and its performance metrics are analysed.

Objective: Globally, cardiovascular diseases (CVDs) are one of the most leading causes of death. In medical screening and diagnostic procedures of CVDs, electrocardiogram (ECG) signals are widely used. Early detection of CVDs requires acquisition of longer ECG signals. It has triggered the development of personal healthcare systems which can be used by cardio-patients to manage the disease. These healthcare systems continuously record, store, and transmit the ECG data via wired/wireless communication channels. There are many issues with these systems such as data storage limitation, bandwidth limitation and limited battery life. Involvement of ECG data compression techniques can resolve all these issues. Method: In the past, numerous ECG data compression techniques have been proposed. This paper presents a methodological review of different ECG data compression techniques based on their experimental performance on ECG records of the Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) arrhythmia database. Results: It is observed that experimental performance of different compression techniques depends on several parameters. The existing compression techniques are validated using different distortion measures. Conclusion: This study elaborates advantages and disadvantages of different ECG data compression techniques. It also includes different validation methods of ECG compression techniques. Although compression techniques have been developed very widely but the validation of compression methods is still a prospective research area to accomplish an efficient and reliable performance.

Wireless Capsule Endoscopy (WCE) has been widely accepted due to its painless method of imaging the entire gastrointestinal tract. In this paper, we propose deep Convolutional Neural Network(CNN) for automatic discrimination of ulcers on different ratios of augmented datasets ranging from 1000 to 10000 WCE images comprising of ulcer and non-ulcer images. A detailed investigation of network configuration for various nodes and depth were performed. The proposed network architecture of four convolutional layers with ( 3 * 3 ) convolutional filters demonstrated significant improvement in terms of performance. The WCE images were obtained from publicly available WCE datasets and real-time WCE video frames. The test results were subjected to hyper-parameter optimization for various tweaking parameters such as epochs, pooling schemes, learning rate, number of layers, optimizer, activation functions and drop out scheme. The experimental results were compared with ten different machine learning classifiers, demonstrating higher prediction performance.

One critical issue for wireless power transfer is to avoid human health impairments caused by electromagnetic radiation (EMR) exposure. The existing studies mainly focus on scheduling wireless chargers so that (expected) EMR at any point in the area does not exceed a threshold Rt. Nevertheless, they overlook the EMR jitter that leads to exceeding of Rt even if the expected EMR is no more than Rt. This paper studies the fundamental problem of RObustly SafE charging for wireless power transfer (ROSE), that is, scheduling the power of chargers so that the charging utility for all echargeable devices is maximized while the probability that EMR anywhere does not exceed Rt is no less than a given confidence. We first build our empirical probabilistic charging model and EMR model. Then, we present EMR approximation and area discretization techniques to formulate ROSE into a Second-Order Cone Program, and the first redundant second-order cone constraints reduction algorithm to reduce the computational cost, and therefore obtain a (1-ϵ)-approximation centralized algorithm. Further, we propose a (1-ϵ)-approximation fully distributed algorithm scalable with network size for ROSE. Simulations and field experiments show that our algorithms can outperform comparison algorithms by 480.19%.

Radio frequency (RF) wireless energy transfer (WET) is a key technology that may allow seamlessly powering future massive low-energy Internet of Things (IoT) networks. To enable efficient massive WET, channel state information (CSI)-limited/free multi-antenna transmit schemes have been recently proposed in the literature. The idea is to reduce/null the energy costs to be paid by energy harvesting (EH) IoT nodes from participating in large-scale time/power-consuming CSI training, but still enable some transmit spatial gains. In this paper, we take another step forward by proposing a novel CSI-free rotary antenna beamforming (RAB) WET scheme that outperforms all state-of-the-art CSI-free schemes in a scenario where a power beacon (PB) equipped with a uniform linear array (ULA) powers a large set of surrounding EH IoT devices. RAB uses a properly designed CSI-free beamformer combined with a continuous or periodic rotation of the ULA at the PB to provide average EH gains that scale as $0.85\sqrt{M}$, where $M$ is the number of PB's antenna elements. Moreover, a rotation-specific power control mechanism was proposed to i) fairly optimize the WET process if devices' positioning information is available, and/or ii) to avoid hazards to human health in terms of specific absorption rate (SAR). We show that RAB performance even approaches quickly (or surpasses, for scenarios with sufficiently large number of EH devices, or when using the proposed power control) the performance of a traditional full-CSI based transmit scheme, and it is also less sensitive to SAR constraints. Finally, we discuss important practicalities related to RAB such as its robustness against non line-of-sight conditions compared to other CSI-free WET schemes, and its generalizability to scenarios where the PB uses other than a ULA topology., Comment: 12 pages, 10 figures. Accepted for publication in IEEE IoT Journal

Software-defined networking (SDN) was devised to simplify network management and automate infrastructure sharing in wired networks. These benefits motivated the application of SDN in wireless sensor networks to leverage solutions for complex applications. However, some of the core SDN traits turn the networks prone to denial of service attacks (DoS). There are proposals in the literature to detect DoS in wireless SDN networks, however, not without shortcomings: there is little focus on resource constraints, high detection rates have been reported only for small networks, and the detection is disengaged from the identification of the type of the attack or the attacker. Our work targets these shortcomings by introducing a lightweight, online change point detector to monitor performance metrics that are impacted when the network is under attack. A key novelty is that the proposed detector is able to operate in either centralized or distributed mode. The centralized detector has very high detection rates and can further distinguish the type of the attack (from a list of known attacks). On the other hand, the distributed detector provides information that allows to identify the nodes launching the attack. Our proposal is tested over IEEE 802.15.4 networks. The results show detection rates exceeding $96\%$ in networks of 36 and 100 nodes and identification of the type of the attack with a probability exceeding $0.89$ when using the centralized approach. Additionally, for some types of attack it was possible to pinpoint the attackers with an identification probability over $0.93$ when using distributed detectors.

The scheduling and control of wireless cloud control systems involving multiple independent control systems and a centralized cloud computing platform are investigated. For such systems, the scheduling of the data transmission as well as some particular design of the controller can be equally important. From this observation, we propose a dual channel-aware scheduling strategy under the packet-based model predictive control framework, which integrates a decentralized channel-aware access strategy for each sensor, a centralized access strategy for the controllers, and a packet-based predictive controller to stabilize each control system. First, the decentralized scheduling strategy for each sensor is set in a noncooperative game framework and is then designed with asymptotical convergence. Then, the central scheduler for the controllers takes advantage of a prioritized threshold strategy, which outperforms a random one neglecting the information of the channel gains. Finally, we prove the stability for each system by constructing a new Lyapunov function, and further reveal the dependence of the control system stability on the prediction horizon and successful access probabilities of each sensor and controller. These theoretical results are successfully verified by numerical simulation.

The evolution of wireless body area networks (WBAN) has changed the human life for its applications in the field of healthcare, fitness, entertainment and sports etc. However, two of the major challenges in the design of WBAN are energy efficiency and connectivity. The placement of relay nodes in a wireless body area network (WBAN) plays an important role in design of energy efficient and reliable WBAN. This problem is a joint problem of data routing and placement of relay nodes and formulated as a linear integer programming model. The main objective of the problem is to minimize the cost of relay nodes, energy consumption and distributing the loads uniformly on the relay nodes. Considering the hardness of the problem, we propose an adaptive cuckoo search based algorithm which uses an efficient fitness function and an adaptive step size proportional to the fitness function for placement of relay nodes. The set of relay nodes obtained by our proposed adaptive cuckoo search algorithm compared with cuckoo search as well as other state of the art algorithms via simulation results. The simulation results reveal that the proposed algorithm not only consumes less energy than its counterparts but also distributes the load evenly on the relay nodes. We consider two different postures of the body with 13 biosensors placed in fixed positions and 50–100 candidate sites for placement of relay nodes. Furthermore, we also consider 80 biosensors randomly deployed in a rectangular area with 50–300 candidate sites to study the scalability of our algorithm.

The rapid development of wireless traffic pushed the wireless community to research different solutions towards the efficient utilization of the available radio spectrum. However, a recent study shows that most of the dynamically allocated spectrum bands, experience significant under-utilization as Cognitive Radio (CR) technology still lacks intelligence. An intelligence in CRs can be incorporated with machine learning algorithms. Further, the perfect Channel State Information (CSI) is hardly obtained and CSI imperfections play a crucial role in Spectrum Management. Thus, for efficient utilization of available spectrum, a decentralized Multi-Agent Reinforcement Learning based resource allocation scheme has been proposed. A robust scheme is proposed which integrates machine learning and CR technology into a sophisticated Multi-Agent System (MAS). Moreover, assisted with cloud computing provides huge amount of storage space, reduces operating expenditures, and provides wider flexibility of cooperation. Hence, to foster the performance of the proposed scheme, a cooperative framework in MAS is introduced which enhances the performance of the proposed scheme in terms of network capacity, outage probability, and convergence speed. Numerical results verify the effectiveness of proposed scheme and show the non-negligible impact of imperfect CSI, thus highlighting the importance of robust designs that maintains users' QoS in practical wireless networks.

The primary constraints of Underwater Wireless Acoustic Sensor Networks (UWASNs) are limited bandwidth, node energy and latency. The process of data aggregation will ease the constraints of UWASN. In this paper, we propose a scheme for data aggregation and routing in UWASN using static and mobile agents based on palm tree structure. The main components of palm tree structure include leaflets, spines, rachis and petioles. The leaflets and smaller leaflets (fronds) are connected to the petiole through spines. The junction of petioles connects to the sink node. The proposed scheme works as follows.Firstly, fronds and leaflets connected to the petioles are created through spines. Secondly, master center nodes are selected on petiole junction using mobile agent based on the parameters such as residual energy, Euclidean distance, petiole angle and connectivity. Thirdly, local center nodes are identified on either side of leaflet and connected to the master center using a mobile agent. Fourthly, the process of local aggregation at local centers happens by taking into account of nodes along the leaflets and carry to a connected master center. Finally, the process of master aggregation at the junction of petioles and delivering the aggregated data to the sink node using a mobile agent is performed. To assess the efficacy of the scheme, simulation in different UWASN scenarios are carried out. The parameters of performance analyzed are master and local center selection time, aggregation ratio, aggregation energy, energy consumption, number of local and master centers involved in the aggregation process and lifespan of the network. We observed that proposed scheme performs better than the existing aggregation scheme.

Emerging wireless applications needs more spectrums space to tackle the rapid growth of users. Outside of radio band in spectrum will be hard to utilize for data transmissions in terms of hardware requirement. Cognitive radio research considers the Current Spectrum underutilization and provides a better model for next-generation wireless environment. Since, the cognitive radio and its policies violate the static spectrum allocation i.e., Current Wireless networks policy, many challenges are in front of us to accomplish a better cognitive radio wireless environment. One of the major challenges is secure transmission and optimal free channel selection for un-interrupt data transmission. In this article, we have proposed an efficient free-channel selection scheme for improving QoS of Cooperative Cognitive Radio Networks. The proposed model has been derived as an integrated approach by using successful MCDM techniques such as EFAHP and TOPSIS. Also, we have proved the optimality of the proposed integrated technique with other parallel channel selection techniques with various scenarios.

Minimized and nearly deterministic end-to-end latency facilitates real-time data acquisition and actuator control. In addition, defined latency is an integral part of quality oriented service in order to get closer to the reliability of wired networks and at the same time take advantage of wireless networking. This paper introduces a QoS routing protocol capable of balancing power consumption between wireless sensor and actuator nodes while minimizing end-to-end latency. We introduce a TDMA scheme in the routed wireless network to enable defined latency and in addition it improves the energy efficiency by avoiding collisions which eliminates time and energy consuming retries. Our novel routing method allows latency and round-trip times to be calculated in advance. We implemented a demonstrator and show experimental results of a wireless sensor network with our proposed routing scheme.

In this article, we present a wireless powered mobile-edge computing system consisting of a hybrid access point and multiple cooperative fogs, where the users in each cooperative fog can share communication and computation resources to improve their computation performance. Based on the classic time-division-multiple-access protocol, we propose a harvest-and-offload protocol to jointly schedule wireless energy transfer and cooperative computation offloading. We minimize the total energy consumption of the system by jointly considering energy beamforming, time-slot assignment, computation-task allocation, and the optimization of central processing unit (CPU) frequencies for computing. We transform the original nonconvex problem to a convex model via utilizing the variable substitution and the semidefinite relaxation methods, and then derive the optimal solution in a semiclosed form via exploiting the Lagrangian method. The extensive numerical results show that the proposed joint communication and computation cooperation scheme can reduce the total energy consumption considerably compared to the state of the art. Moreover, we demonstrate that the dynamic CPU frequency has a positive impact on energy saving compared with the case of fixed CPU frequency.

Just as rank-metric or Gabidulin codes may be used to construct rate-diversity tradeoff optimal space-time codes, a recently introduced generalization for the sum-rank metric -- linearized Reed-Solomon codes -- accomplishes the same in the case of multiple fading blocks. In this paper, we provide the first explicit construction of minimal delay rate-diversity optimal multiblock space-time codes as an application of linearized Reed-Solomon codes. We also provide sequential decoders for these codes and, more generally, space-time codes constructed from finite field codes. Simulation results show that the proposed codes can outperform full diversity codes based on cyclic division algebras at low SNRs as well as utilize significantly smaller constellations.

In this contemporary era, recent developments have seen proliferation of methods on improving energy utilization for wireless sensor networks. Although the proposed methods can mitigate the problem of rapid battery depletion to some extent, the lifespan of sensor nodes is still a biggest constraint. Many studies conducted so far have shown continuous network functions with the help of external harvesting techniques. However, these provide low output because of the limitation on the energy capturing devices. Therefore, energy limitation strongly restricts the usage of wireless sensor nodes. This paper aims to provide high recharging rates and better energy efficiency by proposing a three-step mechanism, which is an extension of our existing proposed work J-ERLB (Joint Energy Replenishment and Load Balancing). In a three-step mechanism, topology selection followed by recharging and load balancing are combined together to prolong the lifetime of sensor nodes. Topology selection is performed by implementing J-ERLB on various topologies like ring, star, and cluster. Further, taking advantage of Unmanned Aerial vehicle (UAV) we focus on data collection and high-rate recharging. We have implemented System on Chip (SoC) integrated chip on UAV to achieve goal of perpetual network operations. Finally, the effectiveness of proposed mechanism is tested by comparing its numerical outcomes with existing J-ERLB and J-MERDG (Joint Mobile Energy Replenishment and Data Gathering) techniques. The overall throughput for 3SM is doubled in comparison with the existing techniques, whereas the average remaining energy shows 6 times better savings for the proposed solution in comparison to the existing J-ERLB and J-MERDG when varying data rates is applied.

Over the last few years, we have seen dramatic increase in the demand for radio spectrum in the world due to rapid growth in wireless services and applications. In order to meet the growing demand for radio spectrum, new schemes should be developed for utilizing free spectrum effectively. Cognitive radio (CR) is promising technology that enables solving spectrum scarcity problem effectively. Thus, in this paper, we propose two schemes to motivate primary user (PU) to share spectrum with secondary users (SUs). The key concern of the proposed schemes is maximizing PU’s profit by hiring free spectrum to wealthy SUs. In the eviction scheme, SUs with less profit are expelled from service to serve wealthy SUs. The folding scheme reconfigures spectrum request to serve it. It reduces the size of request if the available size is not sufficient for the request. The simulation results demonstrate that the ability of new schemes to maximize PU’s profit. Apparently, the results confirm the ability of the proposed schemes to utilize the free spectrum efficiently.

Internet of Things (IoT) produces massive heterogeneous data from various applications, including digital health, smart hospitals, automated pathology labs, and so forth. IoT sensor nodes are integrated with the medical equipment to enable the health workers to monitor the patients’ health condition and appliances in real-time. However, due to security vulnerabilities, an unauthorized user can access health-related information or control the IoT nodes attached to the patient’s body resulting in unprecedented outcomes. Due to wireless channels as a medium of communication, IoT poses several threats such as a denial of service attack, man-in-the-middle attack, and modification attack to the IoT networks’ security and privacy. The proposed research presents a lightweight and anonymity-preserving user authentication protocol to counter these security threats. The given scheme establishes a secure session for the legitimate user and prohibits unauthorized users from gaining access to the IoT sensor nodes. The proposed protocol uses only lightweight cryptography primitives (hash) to alleviate the node’s tiny processor burden. The proposed protocol is efficient and superior because it has low computational and communication costs than conventional protocols. The proposed scheme uses password protection to let only the legitimate user access the IoT sensor nodes to obtain the patient’s real-time health report.

With the exponential increase of interconnected communicating devices which make up the Internet of Things (IoT), securing the network transmission and the fifth generation (5G) systems which is the bedrock for IoT concept actualization is becoming more and more challenging. One of the major attacks which poses a great risk to data transmission is the eavesdropper (Eve) attack which occurs in both single input, single output (SISO), multiple input and multiple output (MIMO) systems. Thus, in this study, our focus is to establish a secured connection in a multiple-antenna transmission when the channel state information (CSI) of Eve is unknown to the network users. Our model comprises a secure wireless communication standard where Eve performs either optimal matched filtering (OMF) or a basic matched filtering (BMF) while the transmitting IoT node employs smart jamming strategy in order to compromise the activities of Eve. With respect to this and in attempt to realize maximum privacy, we examined the design of optimal jamming parameters. In the end, the numerical analysis of our investigation indicates that a substantial privacy advantage is achievable while utilizing only full-duplex jamming against using artificial noise from the transmitter only. However, a joint performance of both results shows a higher privacy improvement.

Many IoT devices lack the necessary interfaces (keyboards, screens) for entering passwords or changing default ones. For these devices, bootstrapping trust can be challenging. We address the problem of device pairing in the absence of any shared secrets. Pairing is a two-phase process that requires the mutual authentication between the two parties and the agreement to a common key that can be used to further bootstrap essential cryptographic mechanisms. We propose a secret-free and in-band trust establishment protocol that achieves the secure pairing of commercial off-the-shelf (COTS) wireless devices with a hub. As compared to the state-of-the-art, our protocol does not require any hardware/firmware modification to the devices, or any out-of-band channels, but can be applied to any COTS device. Furthermore, our protocol is resistant to active signal manipulations attacks that include recently demonstrated signal nullification at an intended receiver. These security properties are achieved in-band with the assistance of a helper device such as a smartphone and by exploiting hard-to-forge signal propagation laws. We perform extensive theoretical analysis to verify the security of the proposed protocol. In addition, we validate our theoretical results with experiments using COTS devices and USRP radios.

Water pipelines are one of the most vital structures to convey fresh water for consumption or irrigation over long distances. However, the major problem of water transportation pipeline is leak which can cause water resources loss, possible human injuries, and environmental pollution problems. For that, these pipelines must be carefully and real-time monitored. In this work, we describe a comprehensive framework for existing monitoring water pipeline techniques based on both wired and wireless networks. Furthermore, we elaborate a comparative overview in terms of keys parameters touching monitoring models. After the evaluation of all the existing pipeline monitoring methods, it is much more evident that techniques based on wireless sensors networks have variety and are the best selection for water pipeline monitoring purposes. This study is a guideline to select which model is the most suitable to design a new pipeline monitoring scheme.

The automotive industry is undergoing a transition toward cleaner and more efficient technologies. Electric vehicles (EVs) play a relevant role in this scenario. However, these vehicles continue to suffer from limitations in terms of autonomy and charging times, which can be alleviated with wireless charging technology. This technology facilitates the charging process but has a lower efficiency when compared with conductive chargers. This effect is particularly severe under misalignment conditions and with the usual variations of the battery's electrical features. In order to avoid this disadvantage, this article presents a predictive control algorithm focused on maximizing the charging efficiency. As a novelty, the algorithm sets three configuration parameters in the power converters of the system. The main advantage lies in the fact that it does not need constant monitoring and offers very low response times. This control also improves compatibility with different automobile manufacturers, adding the capacity to work with receivers and batteries with diverse characteristics while complying with international standards. The validity of the proposal is verified by theoretical analysis, simulations, and the experimental results of a 2-kW prototype. The results show the feasibility of the proposed control and the improvement in the efficiency of the system.

An untethered, soft robot using liquid crystal elastomer (LCE) actuators, onboard power, and wireless Bluetooth control was developed. LCE actuators were thermally triggered using Joule heating and demonstrated an ∼5 N force pull capacity per LCE. A20% repeatable strain was demonstrated over100 cycles with minimal loss of strain at high cycle numbers. The LCE actuators were horizontally oriented to maximize conversion of LCE contraction to overall robot movement. A battery and control board were integrated into the body of the robot, which allowed for Bluetooth control of the LCE on/off cycle. System level programming and design were implemented to offset the slow recovery associated with LCE actuators. The multiple LCE actuator legs were programmed to allow individual control of on/off cycles for each leg. LCE leg actuation was alternated between inner and outer legs to provide horizontal movement with minimized loss of motion during the LCE recovery cycle by actuating one set of legs during the recovery cycle of the other set for a maximum movement speed of 1.27 cm/min. Path control was also demonstrated by turning the robot by actuating two LCE legs on one side of the robot. The robot was able to pull up to 1400 g in ideal frictional conditions, allowing the possibility of payload transport, additional battery storage, or onboard sensors. Additional design considerations are discussed to further improve overall robot speed in the future by combining system and material level design considerations.

Recently, the security problem of wireless networked control systems (WNCSs) has become critical. In this article, we focus on strategies designing problem for sensor and denial-of-service (DoS) attacker in WNCS based on linear quadratic Gaussian (LQG) cost. In this scenario, a sensor measures the output of the system and transmits its local state estimate to the remote estimator via an unreliable channel which may suffer interference from an intelligent DoS attacker. In each step, the sensor demands to determine the power to transmit its packet data, at the same time, the attacker needs to determine the interference power to degrade the performance of the WNCS. To analyze this interactive decision-making process, we construct a two-player zero-sum stochastic game and propose an improved LQG cost considering energy-efficient factors. Then, for continuous power-level setting, we discuss the existence and the structure of equilibrium strategies of the sensor-attacker game. For a discrete power set, an algorithm is developed to solve equilibrium strategies for both players. Finally, we present an example to illustrate our results.

The demand in the biomedical field for fast and precise devices for in vitro applications has increased in recent years. Mobile microrobots are significantly suitable for such applications and are developing rapidly. These microrobots offer untethered actuation toward a contamination-free environment while allowing for fast and precise handling of biological entities for applications such as positioning, sensing, delivery, and cell surgery that are highly effective for new drug discoveries and improving our understanding of cells’ behavior on the single-cell level. Here, we present a review of the recent state of the art in the actuation and implementation of mobile microrobots for in vitro applications. We first explore the widely used methods of wireless actuation. Next, we address the challenge of implementing an on-board interaction technique to handle the target biological entity without affecting the actuation of the microrobot. Finally, we discuss the future directions that would draw the basic outline for the next generation of mobile microrobots for in vitro applications.

This article studies a security issue in remote distributed consensus estimation where sensors transmit their measurements to remote estimators via a wireless communication network. The relative entropy is utilized as a stealthiness metric to detect whether the data transmitted through the wireless network are attacked. The performance degradation induced by an attacker that attempts to be stealthy or undetected is analyzed, and the corresponding false-data attack strategy is characterized, which can achieve the maximal integrated mean-square error (IMSE). Finally, the tradeoff between the performance degradation and attack stealthiness level is evaluated through an example.

Compress-forward (CF) relays can improve communication rates even when the relay cannot decode the source signal. Efficient implementation of CF is a topic of contemporary interest, in part because of its potential impact on wireless technologies such as cloud-RAN. There exists a gap between the performance of CF implementations in the high spectral efficiency regime and the corresponding information theoretic achievable rates. We begin by re-framing a dilemma causing this gap, and propose an approach for its mitigation. We utilize trellis coded quantization (TCQ) at the relay together with multi-level coding at the source and relay, in a manner that facilitates the calculation of bit LLRs at the destination for joint decoding. The contributions of this work include designing TCQ for end-to-end relay performance, since a distortion-minimizing TCQ is suboptimum. The reported improvements include a 1dB gain over prior results for PSK modulation.

The wireless propagation model is important for accurate 5 G network deployment. However, the traditional wireless propagation model is faced with the problems of limited application scenarios, unstable prediction results and high marginal cost of improving accuracy. In order to solve these problems, this paper constructs new features from the original data from different angles, and uses the random forest model to select the core features, which are used to train the fusion model based on the linear weighted summation of regression models such as KNN, LightGBM, and Bagging. After training, the final fusion model is obtained, it solves the problems faced by traditional wireless propagation models. The results and analysis show that the fusion model outperforms the traditional wireless propagation models and the single models that constitutes the fusion model in terms of prediction accuracy and stability, and is not limited by scenarios and easy to deploy.

The fifth-generation (5G) of mobile standards is expected to provide ultra-reliability and low-latency communications (URLLC) for various applications and services, such as online gaming, wireless industrial control, augmented reality, and self driving cars. Meeting the contradictory requirements of URLLC, i.e., ultra-reliability and low-latency, is considered to be very challenging, especially in bandwidth-limited scenarios. Most communication strategies rely on hybrid automatic repeat request (HARQ) to improve reliability at the expense of increased packet latency due to the retransmission of failing packets. To guarantee high-reliability and very low latency simultaneously, we enhance HARQ retransmission mechanism to achieve reliability with guaranteed packet level latency and in-time delivery. The proposed non-orthogonal HARQ (N-HARQ) utilizes non-orthogonal sharing of time slots for conducting retransmission. The reliability and delay analysis of the proposed N-HARQ in the finite block length (FBL) regime shows very high performance gain in packet delivery delay over conventional HARQ in both additive white Gaussian noise (AWGN) and Rayleigh fading channels. We also propose an optimization framework to further enhance the performance of N-HARQ for single and multiple retransmission cases.

In this paper, we study the problem of federated learning over a wireless channel with user sampling, modeled by a fading multiple access channel, subject to central and local differential privacy (DP/LDP) constraints. It has been shown that the superposition nature of the wireless channel provides a dual benefit of bandwidth efficient gradient aggregation, in conjunction with strong DP guarantees for the users. Specifically, the central DP privacy leakage has been shown to scale as $\mathcal {O}(1/K^{1/2})$ , where $K$ is the number of users. It has also been shown that user sampling coupled with orthogonal transmission can enhance the central DP privacy leakage with the same scaling behavior. In this work, we show that, by jointly incorporating both wireless aggregation and user sampling, one can obtain even stronger privacy guarantees. We propose a private wireless gradient aggregation scheme, which relies on independently randomized participation decisions by each user. The central DP leakage of our proposed scheme scales as $\mathcal {O}(1/K^{3/4})$ . In addition, we show that LDP is also boosted by user sampling. We also present analysis for the convergence rate of the proposed scheme and study the tradeoffs between wireless resources, convergence, and privacy theoretically and empirically for two scenarios when the number of sampled participants are $(a)$ known, or $(b)$ unknown at the parameter server.

The dynamic control of wireless channels is a trending field of research which has been made possible by the advent of metamaterial-based reconfigurable intelligent surfaces (RIS). Moreover, for future applications in 5G and beyond, the device size constraints and power supply issues also need to be resolved before RIS can be deployed at large scale. Keeping these problems in mind, we propose RIS-assisted ambient backscattering (ABSc) communication framework utilizing advanced spatial modulation (ASM) techniques for spectral-and energy-efficient communication. Here, ABSc technique is leveraged for harnessing the power from ambient RF waves, while ASM techniques are deployed to ensure high spectral efficiencies by activating more than one but not the full set of antennas for transmission. Extensive performance analysis of ASM techniques are carried out, and upper-bounds on the ensuing error rates are derived. We corroborate the error rate upper-bound results with Monte-Carlo simulation results, and also provide comparative results to reveal the benefits of the proposed framework. In particular, it is shown that the ASM technique of improved quadrature spatial modulation (IQSM) can achieve the highest spectral efficiency. All the ASM techniques can perform better than the conventional spatial modulation technique by offering signal-to-noise power ratio gains of more than 2 dB. Additionally, RIS-assisted ASM techniques with ABSc perform better than the conventional frameworks without RIS.

The next-generation of wireless networks will enable many machine learning (ML) tools and applications to efficiently analyze various types of data collected by edge devices for inference, autonomy, and decision making purposes. However, due to resource constraints, delay limitations, and privacy challenges, edge devices cannot offload their entire collected datasets to a cloud server for centrally training their ML models or inference purposes. To overcome these challenges, distributed learning and inference techniques have been proposed as a means to enable edge devices to collaboratively train ML models without raw data exchanges, thus reducing the communication overhead and latency as well as improving data privacy. However, deploying distributed learning over wireless networks faces several challenges including the uncertain wireless environment (e.g., dynamic channel and interference), limited wireless resources (e.g., transmit power and radio spectrum), and hardware resources (e.g., computational power). This paper provides a comprehensive study of how distributed learning can be efficiently and effectively deployed over wireless edge networks. We present a detailed overview of several emerging distributed learning paradigms, including federated learning, federated distillation, distributed inference, and multi-agent reinforcement learning. For each learning framework, we first introduce the motivation for deploying it over wireless networks. Then, we present a detailed literature review on the use of communication techniques for its efficient deployment. We then introduce an illustrative example to show how to optimize wireless networks to improve its performance. Finally, we introduce future research opportunities. In a nutshell, this paper provides a holistic set of guidelines on how to deploy a broad range of distributed learning frameworks over real-world wireless communication networks.

This paper discusses the key technologies for the fusion of Radio Frequency Identification (RFID) technology and wireless sensor networks through a fusion approach; meanwhile, it addresses the problem of energy imbalance infusion networks. This paper introduces the network energy consumption calculation methods under direct transmission and minimum energy multi-hop routing protocols, and designs an improved network structure model to improve the communication scale. The RFID reading and writing system based on Zig Bee technology is developed, and focuses on the hardware interface circuit design of the fusion system, the development of compatible communication protocols, the anti-collision algorithm, and the transmission performance of the system under two outdoor environments. The design of the environmental collection terminal uses relevant sensors to collect the transport environment data in the carriage. And Wireless Sensor Network (WSN) combined with General Packet Radio Service (GPRS) technology realizes the function of the main aggregation node of wireless sensors. The method achieves data aggregation and data transmission, and the development of the application platform of the monitoring system uses software technology. Based on the browser/server (B/S) architecture, the development of the monitoring system is finally realized. Finally, the cold chain transportation simulation experiment was conducted to test the system, and the results showed that the hardware of the cold chain coordination monitoring system can work normally and the software design of the monitoring system can meet the basic requirements of the system for the third-party logistics enterprises. The efficiency of our results is improved by about 10% compared to other studies, and their accuracy is improved to a higher degree, which makes our model more practical.

The study on the multirobot collaborative control method has broad application prospect and value in the field of robotics. To adapt to a variety of working environments and higher efficiency, this article designed the autonomous multirobot collaborative control system based on spherical amphibious robots. First, this article proposed the formation strategy for spherical amphibious multirobot and used linear quadratic regulator algorithm to adjust the robot trajectory. Then, this article used XBee wireless communication with excellent communication performance under low noise ratio conditions to complete real-time information sharing of multirobot. Finally, based on the theoretical analysis and calculation, a series of land and underwater evaluation experiments were carried out to verify the feasibility of autonomous multirobot collaborative control system. The experimental results showed that the spherical amphibious multirobot could realize collaborative control accurately under different environments according to the predetermined trajectory.

IEEE 802.11p standard defines wireless technology protocols that enable vehicular transportation and manage traffic efficiency. A major challenge in the development of this technology is ensuring communication reliability in highly dynamic vehicular environments, where the wireless communication channels are doubly selective, thus making channel estimation and tracking a relevant problem to investigate. In this paper, a novel deep learning (DL)-based weighted interpolation estimator is proposed to accurately estimate vehicular channels especially in high mobility scenarios. The proposed estimator is based on modifying the pilot allocation of the IEEE 802.11p standard so that more transmission data rates are achieved. Extensive numerical experiments demonstrate that the developed estimator significantly outperforms the recently proposed DL-based frame-by-frame estimators in different vehicular scenarios, while substantially reducing the overall computational complexity., 16 pages

Shared spectrum usage is inevitable due to the ongoing increase in wireless services and bandwidth requirements. Spectrum monitoring is a key enabler for efficient spectrum sharing by multiple radio access technologies (RATs). In this paper, we present signal classification using deep neural networks to identify various radio technologies and their associated interferences. We use Convolutional Neural Networks (CNN) to perform signal classification and employ six well-known CNN models to train for ten signal classes. These classes include LTE, Radar, WiFi and FBMC (Filter Bank Multicarrier) and their interference combinations, which include, LTE+Radar, LTE+WiFi, FBMC+Radar, FBMC+WiFi, WiFi+Radar and Noise. The CNN models include, AlexNet, VGG16, ResNet18, SqueezeNet, InceptionV3 and ResNet50. The radio signal data sets for training and testing of CNN-based classifiers are acquired using a USRP-based experimental setup. Extensive measurements of these radio technologies (LTE, WiFi, Radar and FBMC) are done over different locations and times to generate a robust dataset. We propose a novel (spectrogram) representation called the Quarter-spectrogram (Q-spectrogram) that squeezes temporal and frequency information for input to CNN models. While considering classification accuracy, model complexity and prediction time for a single input Q-spectrogram (image), ResNet18 (CNN model) gives the best overall performance with 98% classification accuracy. While SqueezeNet (CNN model) offers the lowest model complexity which makes it very suitable for resource-constrained radio monitoring devices and also offers the least prediction time of 110 msec. Moreover, we also propose a simple WiFi classification scheme that buffers several WiFi Q-spectrograms and then makes a decision about WiFi’s presence and also gives a quantified measure of WiFi traffic density.

An integration of nonorthogonal multiple access (NOMA) in device-to-device (D2D) mobile groups (DMGs) generates a large amount of energy and delay due to sharing the same subcarrier. In this article, we investigate a distributed energy-efficient and delay tradeoff scheme for NOMA-based DMGs in wireless powered communication networks. The stochastic optimization problem is formulated to maximize the energy efficiency and minimize the delay. Many-to-one matching-based subcarrier allocation, weighted sum method, and Lyapunov optimization scheme are used to investigate the possible solution. Further, to obtain the closed-form expression of power and to optimize the time for wireless powered communication networks, we use Lagrangian function. Simulation results show that the proposed scheme achieves 2.6% and 15.78% superior performance than the existing NOMA and orthogonal frequency division multiple access (OFDMA) schemes under the condition of constant circuit power consumption.

Femtocell access point gives an efficient solution to furnish peak data rates in indoor wireless access systems. Propagation of the signal will get up-shot because of few considerations in advanced communication appliances. The maximum simulation reflection coefficient ( S 11 ) of proposed antenna is 45.12 dB, directivity is 6.54 dBi with the efficiency of 60% and at 60 GHz, and the measurement value of 36.35 is observed which covers the complete RADAR applications which have an unlicensed bandwidth. The proposed tree-shaped unequal slotted antenna is well suited for millimeter-wave applications because of its good bandwidth and less dimension.