Emergency Packets Research Articles

RLS2: An energy efficient reinforcement learning- based sleep scheduling for energy harvesting WBANs

In the Energy-Harvesting Wireless Body Area Networks (EH-WBAN), one of the fundamental challenges is preserving the self-sustainability of sensors without compromising network reliability and connectivity. Determining the sleep/wake schedule of body nodes (BNs) is an efficient way to achieve self-sustainability. Sleeping nodes should be connected to at least one active node to reduce delay and keep the network connected. There are two fundamental problems with previous methods for determining BN's sleep/wake schedule: (1) BN suffers from emergency packet loss and unnecessary frequent sleeping and waking up, and (2) They do not guarantee network connectivity. Studies that have only examined connectivity in EH-WBAN also have two main issues: (1) BNs are considered homogenous in terms of energy harvesting and its consumption, (2) These methods cannot adapt to the time-varying behavior of energy-harvesting resources. This study proposes a new method for sleep/wake scheduling called Reinforcement Learning-based Sleep Scheduling (RLS2). RLS2 has the following innovative points: (1) To avoid emergency packet loss or unnecessary frequent sleeping and waking up, each BN has its own sleep/wake schedule based on its energy level and sensed data changes, (2) Lowest possible number of BNs are determined as relay nodes in each round to increase network reliability and connectivity; these BNs remain active in each round, while the others operate according to the determined schedule. In this part of the proposed method: (1) Heterogeneous BNs are considered, (2) As a first step in solving adaptability, the problem of finding the optimal active groups is formulated as a Markov decision process (MDP), followed by a Q-learning algorithm capable of learning time-varying behavior of energy harvesting resources, (3) The unavailable action space is removed to reduce the problem's complexity, (4) To achieve good Q-learning performance, a reward function based on residual energy level and neighborhood degree of BNs is defined. It can find an active group with the lowest cardinality in the current round, which is maximum in terms of the residual energy of its sensors. The performed simulations indicate the appropriate convergence of the proposed method. The results show that, on average, the proposed method improves network connectivity and energy efficiency by 50% and 31%, respectively, and reduces network delay by 27%.

RBDMS: Rate-Adaptation and Buffer-Awareness Data Gathering for Mobile Sink Scheduling in WSNs

Employing a mobile sink (MS) to act as a relay node in wireless sensor network (WSN) applications is a promising solution for efficient power saving and data collection. However, establishing long-distance traveling leads to larger latency or inefficient buffer management at rendezvous points (RPs), e.g., flying UAVs in disaster management. Moreover, there is no efficient solution to guarantee the completeness of data gathering by considering the waiting time (sojourn) of the MS to receive packets from RPs in addition to the MS moving time among RPs. This work presents a rate-adaptation and buffer-awareness data gathering for MS scheduling (RBDMS) by constructing grid cells in the monitoring area. In fact, it establishes the shortest path by passing within the communication range of the sensors based on data volume. RBDMS not only has a mechanism for handling emergency packets with low latency but also benefits from stochastic integer programming (SIP) for scheduling the MS sojourn time with lower computational time using Lagrangian relaxation. Simulation results confirm that the proposed RBDMS outperforms comparable state-of-the-art works in terms of the MS path length, network lifetime, the energy consumption of sensors, and MS, as well as buffering performance.

RELIABLE ROUTING OF EMERGENCY DATA IN WIRELESS BODY AREA NETWORKS UNDER STATIC AND MOBILE SCENARIOS

Timely intervention can help save precious lives of people who are at risk from chronic diseases or fatal diseases such as cardiac arrest, heart diseases to mention a few. These are the major causes of disability and death across the globe. Continuous monitoring of vital body parameters can be useful in timely intervention which can save the lives of the patients suffering from chronic diseases which can be life threatening, also improve the quality of life. This can be done using Wireless Body Area Networks (WBAN) with sensors placed on human body. This network uses routing protocol to transmit the sensed data packets to the destination node. if the detected information falls into the category of emergency data, It is critical for sensor nodes to transmit data in a reliable and timely manner while consuming as little energy as possible. Existing protocols propose routing methods that classify traffic and prioritize emergency data transmission. However, apart from traffic classification, robust measures to reduce the loss of emergency packets at various levels during transmission have not been adequately addressed. This can reduce the reliability of emergency data, also increasing the traffic load and energy consumption in forwarder nodes. This shortcoming drives us to propose a routing protocol EER-QR (Energy Efficient Routing with QoS and Reliability) which uses specialized modules with measures for a specific task. Experimental results have demonstrated that EER-QR is Capable of reliably transmitting emergency data, minimizing network load and therefore extending network lifetime. This can help with proper diagnosis in such critical situations resulting in timely intervention and treatment saving the lives of patients.

Edge device based efficient data collection in smart health monitoring system using wireless body area network

Wireless Body Area Network (WBAN) provides continuous remote patient health monitoring without disrupting their normal daily activities. In WBAN, the bio-sensor is placed on the human body to detect vital changes due to the abnormal behavior of human organs and send the sensed data via the Internet to the medical/cloud server. However, due to the processing of huge amounts of data generated by the EEG and ECG sensor, resource-limited networks face a big challenge in satisfying the required Quality of Services (QoS). Moreover, the WBAN often meets the major problem of QoS degradation due to congestion. In this paper, Minimum Edge-shared Vertex Path Selection (MEVPS) for WBAN seeks to mitigate the congestion problem and decreases the latency during emergency packets delivery. A minimum edge-shared vertex path between the source and the Base Station (BS) is created by updating neighbor information using Ant Colony Algorithm (ACA). In addition, the body controller in the proposed system minimizes the data traffic entered into the network through the use of packet discard policy. It is observed from the simulation results that the proposed routing strategy demonstrates a better efficacy of QoS parameters.

A Highly Effective Route for Real-Time Traffic Using an IoT Smart Algorithm for Tele-Surgery Using 5G Networks

Nowadays, networks use many different paths to exchange data. However, our research will construct a reliable path in the networks among a huge number of nodes for use in tele-surgery using medical applications such as healthcare tracking applications, including tele-surgery which lead to optimizing medical quality of service (m-QoS) during the COVID-19 situation. Many people could not travel due to the current issues, for fear of spreading the covid-19 virus. Therefore, our paper will provide a very trusted and reliable method of communication between a doctor and his patient so that the latter can do his operation even from a far distance. The communication between the doctor and his/her patient will be monitored by our proposed algorithm to make sure that the data will be received without delay. We test how we can invest buffer space that can be used efficiently to reduce delays between source and destination, avoiding loss of high-priority data packets. The results are presented in three stages. First, we show how to obtain the greatest possible reduction in rate variability when the surgeon begins an operation using live streaming. Second, the proposed algorithm reduces congestion on the determined path used for the online surgery. Third, we have evaluated the affection of optimal smoothing algorithm on the network parameters such as peak-to-mean ratio and delay to optimize m-QoS. We propose a new Smart-Rout Control algorithm (s-RCA) for creating a virtual smart path between source and destination to transfer the required data traffic between them, considering the number of hops and link delay. This provides a reliable connection that can be used in healthcare surgery to guarantee that all instructions are received without any delay, to be executed instantly. This idea can improve m-QoS in distance surgery, with trusted paths. The new s-RCA can be adapted with an existing routing protocol to track the primary path and monitor emergency packets received in node buffers, for direct forwarding via the demand path, with extended features.

Towards Optimal Dissemination of Emergency Messages in Internet of Vehicles: A Dynamic Clustering-Based Approach

In this paper, we target dissemination issues of emergency messages in a highly dynamic Internet of Vehicles (IoV) network. IoV is emerging as a new class of vehicular networks to optimize road safety as well as users’ comfort. In such a context, forwarding emergency messages through vehicle-to-vehicle communications (V2V) plays a vital role in enabling road safety-related applications. For instance, when an accident occurs, forwarding such information in real time will help to avoid other accidents in addition to avoiding congestion of network traffic. Thus, dissemination of emergency information is a major concern. However, on the one hand, vehicle density has increased in the last decade which may lead to several issues including message collisions, broadcast storm, and the problem of hidden nodes. On the other hand, high mobility of vehicles and hence dynamic changes of network topology result in failure of dissemination of emergency packets. To overcome these problems, we propose a new dissemination scheme of emergency packets by vehicles equipped with both DSRC and cellular LTE wireless communication capabilities. Our scheme is based on a dynamic clustering strategy, which includes a new cluster head selection algorithm to deal with the broadcast storm problem. Furthermore, our selection algorithm enables not only the election of the most stable vehicles as cluster heads, and hence their exploitation in forwarding the emergency information, but also the avoidance of packet collisions. We simulated our scheme in an urban environment and compared it with other data dissemination schemes. Obtained results show the efficiency of our scheme in minimizing collision and broadcast storm problems, while improving latency, packet delivery ratio and data throughput, as compared to other schemes.

EWPS: Emergency Data Communication in the Internet of Medical Things

In this article, we explore the remote monitoring of patients in an on-demand service that can be implemented using the Internet of Medical Things (IoMT). Network congestion and delay are important issues that must be resolved to handle emergencies in IoMT. Backpressure scheduling is a well-known scheme to resolve network congestion and improve network throughput for emergency data packets. Traditional backpressure scheduling is known to categorize each packet and schedules emergency packets to be forwarded faster than regular packets. However, in large-scale networks, these scheduling algorithms find unnecessarily long paths. This leads to high end-to-end delay and decreases the performance of guaranteed delivery. To resolve these issues, this article proposes an event-aware priority scheduling algorithm for data packets. This model follows a single priority queue model to manage all packets and emergency packets have been identified by the threshold values. Also, a separate communication path has been identified as well to reduce the waiting time for each category of the packet. Meanwhile, to reduce delay in packet communication, a delay-efficient data aggregation tree is constructed, which is combined with the priority queue model. Our in-depth simulation results of the proposed model prove the novel contribution to reducing delay in emergency packet delivery and avoiding network congestion. Moreover, the proposed model is also compared with the existing state-of-the-art models to show its ability to outperform similar methodologies.

Efficient Immediate-Transmission Scheme for Emergency Packets in Closed Area Wireless Access Networks

Efficient Immediate-Transmission Scheme for Emergency Packets in Closed Area Wireless Access Networks

Priority-Aware Fast MAC Protocol for UAV-Assisted Industrial IoT Systems

Many hazardous industrial incidents can occur due to the inadequate and inefficient monitoring of the offshore plants. Manual inspections of the offshore plants on a regular basis is not only time consuming but also dangerous regarding to human safety. For considering the safety measurement and alleviating the burden of the manual inspection, unmanned aerial vehicles (UAVs) can be effectively utilized to collect data from the remote industrial environment. In an industrial scenario, less delay is required for emergency packets and high throughput is needed for monitoring packets. This paper proposes a priority-aware fast MAC (PF-MAC) protocol for UAV-assisted industrial Internet-of-things (IIoT) systems, ensuring fast and robust data delivery. At first, the IoT devices under the UAV communication range transmit a reservation frame to the UAV to catch transmission opportunities using CSMA/CA. The devices utilize static traffic priority and a novel adaptive backoff mechanism during CSMA/CA. After receiving the reservation frames from the IoT devices, the UAV calculates the dynamic device priority based on their static traffic priority, communication duration, sampling frequency, and remaining energy. Then, time slot is assigned by the UAV to each device for data transmission. To ensure fairness, if a device fails in contention during the CSMA/CA period, the static traffic priority is raised in the next retransmission. There is no prior work in the literature that considers both the traffic priority and the device priority to ensure Quality of Service in IIoT and related systems. According to our performance study, the proposed PF-MAC outperforms the conventional protocols in terms of delay and throughput.

An Energy Efficient QoS Supported Optimized Transmission Rate Technique in WBANs

The Wireless Body Area Networks (WBANs) provide an unprecedented opportunity for ubiquitous real-time health-care and fitness monitoring without impairing the activities of the user. Furthermore, meeting the quality of service (QoS) requirements, i.e., throughput, delay and packet loss rate in WBAN, is a challenge due to the lossy channel, resource-restricted nodes and the transmission of data in the highly dynamic situations. In the proposed work, an optimized hybrid technique of Genetic algorithm (GA) with BAT algorithm (GABAT) is presented to achieve QoS metrics. The hybrid GABAT meticulously adjusts the transmission rates at each sensor node for each posture taking into account both the QoS metric constraint and the dynamic link constraint. The proposed scheme gives higher priority to the emergency packets than the normal packets to support the QoS requirements. The results exhibit that the proposed GABAT attains maximum fitness value in less iterations as compared to GA and BAT optimizations.

Prioritized data based Energy Efficient MAC protocol in Wireless Body Area Network

Wireless Body Area Network (WBAN) is an exclusively designed Wireless Sensor Networks that used in today’s health-care system. The central challenge in WBAN is to transfer the medical data with limited energy and with high reliability. The IEEE 802.15.4 MAC Protocol is a standard model used to consume less energy by providing low data rate. This paper aimed to present a novel protocol PD-MAC, an enhanced version of IEEE 802.15.4 to achieve the above goal. The main objective of this protocol is to transmit the packets according to their priorities. It also improves the retransmission and packet drop process by introducing an additional slot to define Starvation Index in the super-frame of IEEE 802.15.4. A node has to start its transmission when the timer is set to zero. A node has to sense the channel status before transmission begins. The data are transmitted according to their priorities only when it senses the free channel. However if the channel is not free then retransmission of packet will be carried out and in each retransmission process the starvation index increments the priority of the packet. When the packet priority raises to high then it transmits the packet by considering it as high emergency packet. For energy efficiency a max limit is define to retransmit a data packet. This protocol has been simulated using Castalia 3.2 environment and the result validate that our proposed protocol provides better service in terms of least Packet Delay and lowest Energy Consumption to its counterparts.

Optimal packet routing for wireless body area network using software defined network to handle medical emergency

<span lang="EN-US">The packet forwarding node selection is one of the main constraints in the Software Defined Network (SDN). To improve the network performance, the SDN controller has to choose the shortest and optimised path between source and destination in routine and emergency packet transmission. In e-health service, information of the emergency patient has to be transferred immediately to remote hospitals or doctors by using efficient packet routing approach in Wireless Body Area Network (WBAN). In WBAN, to improve the packet transmission, the optimal packet routing policy developed based on packets priority with the support of a greedy algorithm for SDN. The SDN Controller selects the forwarding node based on node propagation delay and available bandwidth between two forwarding nodes. The mesh network topology network created for implementation, implementation results are compared with existing research works. Finally, this algorithm implemented in our institution, Software defined communication testbed laboratory (SDCTestbed Lab) with the support of 13 Zodiac-Fx (Forwarding device), 2 Raspberry-Pi3 B+ Model (host) and Arduino kit (sensor node).</span>

Adaptive QoS constraint-based service differentiated routing in wireless sensor network

Achieving the best quality of service (QoS) as per user requirement is one of the important challenges. The time-critical applications in wireless sensor networks (WSNs) demand energy efficient transmission of data with limited resource availability. To resolve these issues, AQSDR has been proposed. The proposed protocol support packet differentiation and selection of sensor node based on energy, delay, and congestion for path establishment to transfer normal data packets providing energy conservation. The multipath is chosen for transmission of emergency packets satisfying delay requirements of non-delay tolerant applications. AQSDR support adaptive path selection, according to application requirement. The proposed technique in this work outperforms when compared with the existing protocol in terms of minimised energy consumption, delay, control overhead, packet drop ratio, and high throughput.

Queue size estimation of nodes in a heterogeneous ocean network with multiple priority traffic

Ocean network is a heterogeneous wireless network of fishing vessels at sea to provide internet over sea. The types of messages exchanged between fishing vessels include emergency data, normal data, voice and video messages. As the connectivity is intermittent, it is necessary to select the most appropriate message from the transmission queue for forwarding. This paper focuses on designing a queuing model with multiple priorities to estimate the queue size of each type of message in a node. Solving the steady state equations gives the number of emergency packets, data packets, audio packets and video packets waiting in the node to be transmitted. Hence the message dissemination algorithm can select the appropriate queue for transmitting the message to maximise the available resources with fairness.

A priority-based congestion-avoidance routing protocol using IoT-based heterogeneous medical sensors for energy efficiency in healthcare wireless body area networks

A wireless body area network is a collection of Internet of Things–based wearable heterogeneous computing devices primarily used in healthcare monitoring applications. A lot of research is in process to reduce the cost and increase efficiency in medical industry. Low power sensor nodes are often attached to high-risk patients for real-time remote monitoring. These sensors have limited resources such as storage capacity, battery life, computational power, and channel bandwidth. The current work proposes a multi-hop Priority-based Congestion-avoidance Routing Protocol using IoT based heterogeneous sensors for energy efficiency in wireless body area networks. The objective is to devise a routing protocol among sensor nodes such that it has minimum delay and higher throughput for emergency packets using IoT based sensor nodes, optimal energy consumption for longer network lifetime, and efficient scarce resource utilization. In our proposed work, data traffic is categorized into normal and emergency or life-critical data. For normal data traffic, next-hop selection will be selected based upon three parameters; residual energy, congestion on forwarder node, and signal-to-noise ratio of the path between source and forwarder node. We use the data aggregation and filtration technique to reduce the network traffic load and energy consumption. A priority-based routing scheme is also proposed for life-critical data to have less delay and greater throughput in emergency situations. Performance of the proposed protocol is evaluated with two cutting-edge routing techniques iM-SIMPLE and Optimized Cost Effective and Energy Efficient Routing. The proposed model outperforms in terms of network throughput, traffic load, energy consumption, and lifespan.

ICAFE: Intelligent Congestion Avoidance and Fast Emergency services

Abstract Content Centric Network (CCN) has been envisioned as a paradigm shift from client server architecture. In smart cities, transportation plays an important role where integrated services facilitate citizens through the ease of use, safety, and convenience. In this work, we propose an integrated CCN for intelligent Congestion Avoidance and Fast Emergency (iCAFE) services delivery at road side accidents. One of the significant contributions is a novel content-centric VANET-based protocol called iCAFE, an efficient traffic control algorithm and five unique packet headers for effective communications. In case of accident, emergency packets are broadcast to RSU wherein the forwarding information based (FIB) and pending interest table (PIT) are updated accordingly. The RSU broadcasts interest packets to hospital and sends a rescue message to the ambulance. The RSU also informs nearest RSUs and vehicles to evacuate the affected lane. After the rescue process is completed, the data packet is unicast from the hospital to the RSU and the PIT and FIB are updated. iCAFE achieves a high packet delivery ratio (PDR) with minimum rescue delay (R-Delay), high throughput, minimum network load, smaller collision probability, and minimum packet drop fraction. The iCAFE results are compared with the traffic accidents reduction strategy (TARS).

EABS: An Event-Aware Backpressure Scheduling Scheme for Emergency Internet of Things

The backpressure scheduling scheme has been applied in Internet of Things, which can control the network congestion effectively and increase the network throughput. However, in large-scale Emergency Internet of Things (EIoT), emergency packets may exist because of the urgent events or situations. The traditional backpressure scheduling scheme will explore all the possible routes between the source and destination nodes that cause a superfluous long path for packets. Therefore, the end-to-end delay increases and the real-time performance of emergency packets cannot be guaranteed. To address this shortcoming, this paper proposes EABS, an event-aware backpressure scheduling scheme for EIoT. A backpressure queue model with emergency packets is first devised based on the analysis of the arrival process of different packets. Meanwhile, EABS combines the shortest path with backpressure scheme in the process of next-hop node selecting. The emergency packets are forwarded in the shortest path and avoid the network congestion according to the queue backlog difference. The extensive experiment results verify that EABS can reduce the average end-to-end delay and increase the average forwarding percentage. For the emergency packets, the real-time performance is guaranteed. Moreover, we compare EABS with two existing backpressure scheduling schemes, showing that EABS outperforms both of them.

Priority-based scheduling using best channel in 6TiSCH networks

Many industrial wireless sensor networks deploy the IEEE 802.15.4-2015 standard with low power consumption and the Internet networking capabilities. The time-slotted channel hopping (TSCH) is an amendment to the medium access control protocol defined by the IEEE 802.15.4 standard. Furthermore, the Internet engineering task force (IETF) defines an architecture for IPv6 over the TSCH mode of IEEE 802.15.4 (6TiSCH) to provide deterministic properties on wireless sensor networks. The TSCH leverages the scheduled time slot to achieve low power operation and frequency hopping to combat external interference and multi-path fading. Although TSCH provides the channel hopping mechanism to increase transmission reliability, it is still susceptible to periodic interference for the specific frequency. In this research, we focus on the issue of random channel hopping in the error-prone wireless channel condition and the scheduling for different service requirements. We propose a priority-based scheduling using best channel (P-SBC) mechanism for periodic sensing data and emergency packets transmission in 6TiSCH networks. The simulation results show that the proposed P-SBC achieves higher reliability with significantly lower end-to-end latency for emergency packets transmission as compared to TSCH standard.

Efficient Patient Care Through Wireless Body Area Networks—Enhanced Technique for Handling Emergency Situations with Better Quality of Service

Wireless body area networks (WBAN) is a wireless network of sensors placed in and around the human body for monitoring the patient conditions remotely. The goal of WBAN networks is to report the patient condition to the monitoring system with maximum reliability and minimum delay and deliver the life critical data in the emergency situation with utmost priority. The proposed MAC protocol is aimed at delivery of emergency packets with maximum reliability and minimum delay through the introduction of mini slots in the beacon enabled superframe for exclusive transmission of the same. To improve the packet delivery ratio of the normal packets and decrease the energy consumption of the low data rate nodes, a packet rate based scheduled slot allocation is added to this protocol. Extensive simulations show that the proposed protocol is able to achieve nearly 98% packet delivery ratio and less than 100 ms delay for emergency packets. By varying the number of allocated scheduled slots based on the packet rate of the nodes, the proposed protocol has shown improved performance in the packet delivery ratio (93%) of normal packets as compared to IEEE 802.15.6 (85%), also the energy consumption of low data rate nodes has decreased by 64%. The results show that the proposed protocol is successful in realizing much better delay and packet delivery values for emergency and normal packets.

Transmission-Rate-Adaption Assisted Energy-efficient Resource Allocation with QoS Support in WBANs

With the advances in wearable sensors and wireless technologies, wireless body area network (WBAN) has become a promising network to provide health applications, such as ubiquitous e-Health services and real-time health monitoring. In these WBANs, quality of service (QoS) requirements, including packet loss rate, throughput, and delay, need to be guaranteed for providing reliable and real-time data transmission even under highly dynamic environment due to human mobility and postures. Meanwhile, energy efficiency is another key factor to consider for increasing the network lifetime in such a resource-constrained network. In this paper, a transmission-rate-adaption assisted and energy-efficient resource allocation scheme is proposed in which both constraints of QoS metrics and the characteristics of dynamic links are considered. Specifically, a QoS optimization problem is formulated to optimize the transmission power and time slots for each sensor, which minimizes energy consumption subjected to the QoS constraints. Due to the dynamic link characteristics, the link quality may become poor, and then the QoS requirements of normal packets and emergency packets cannot be satisfied. Besides, the emergency packets should receive more attentions with a high priority and high QoS requirements. To guarantee the QoS requirements when the link is very poor, a transmission rate adaption policy is proposed to carefully adjust the transmission rate at each sensor to assist the QoS optimization problem, and a priority-based retransmission strategy is designed to apply the retransmission strategy to further improve the performances of the emergency packets with a high priority. Numerical results demonstrate the effectiveness of the QoS optimization problem, the proposed transmission rate adaption policy, and the priority-based retransmission strategy.